% This file was created with JabRef 2.5.
% Encoding: UTF8

@ARTICLE{Aamodt2006,
  author = {Aamodt, Geir and Samuelsen, Sven and Skrondal, Anders},
  title = {{A simulation study of three methods for detecting disease clusters}},
  journal = {International Journal of Health Geographics},
  year = {2006},
  volume = {5},
  pages = {15+},
  number = {1},
  abstract = {{BACKGROUND:Cluster detection is an important part of spatial epidemiology
	because it can help identifying environmental factors associated
	with disease and thus guide investigation of the aetiology of diseases.
	In this article we study three methods suitable for detecting local
	spatial clusters: (1) a spatial scan statistic (SaTScan), (2) generalized
	additive models (GAM) and (3) Bayesian disease mapping (BYM). We
	conducted a simulation study to compare the methods. Seven geographic
	clusters with different shapes were initially chosen as high-risk
	areas. Different scenarios for the magnitude of the relative risk
	of these areas as compared to the normal risk areas were considered.
	For each scenario the performance of the methods were assessed in
	terms of the sensitivity, specificity, and percentage correctly classified
	for each cluster.RESULTS:The performance depends on the relative
	risk, but all methods are in general suitable for identifying clusters
	with a relative risk larger than 1.5. However, it is difficult to
	detect clusters with lower relative risks. The GAM approach had the
	highest sensitivity, but relatively low specificity leading to an
	overestimation of the cluster area. Both the BYM and the SaTScan
	methods work well. Clusters with irregular shapes are more difficult
	to detect than more circular clusters.CONCLUSION:Based on our simulations
	we conclude that the methods differ in their ability to detect spatial
	clusters. Different aspects should be considered for appropriate
	choice of method such as size and shape of the assumed spatial clusters
	and the relative importance of sensitivity and specificity. In general,
	the BYM method seems preferable for local cluster detection with
	relatively high relative risks whereas the SaTScan method appears
	preferable for lower relative risks. The GAM method needs to be tuned
	(using cross-validation) to get satisfactory results.}},
  citeulike-article-id = {665742},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-5-15},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/16608532},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=16608532},
  doi = {10.1186/1476-072X-5-15},
  issn = {1476-072X},
  keywords = {cluster, disease, gam, model, satscan, simulation},
  owner = {ijt1},
  posted-at = {2006-05-22 20:06:47},
  priority = {3},
  timestamp = {2011.01.19},
  url = {http://dx.doi.org/10.1186/1476-072X-5-15}
}

@ARTICLE{citeulike:8445794,
  author = {Abrams, Allyson and Kleinman, Ken and Kulldorff, Martin},
  title = {{Gumbel based p-value approximations for spatial scan statistics}},
  journal = {International Journal of Health Geographics},
  year = {2010},
  volume = {9},
  pages = {61+},
  month = {December},
  abstract = {{Background:\&\#10;The spatial and space-time scan statistics are
	commonly applied for the detection of geographical disease clusters.
	Monte Carlo hypothesis testing is typically used to test whether
	the geographical clusters are statistically significant as there
	is no known way to calculate the null distribution analytically.
	In Monte Carlo hypothesis testing, simulated random data are generated
	multiple times under the null hypothesis, and the p-value is r/(R+1),
	where R is the number of simulated random replicates of the data
	and r is the rank of the test statistic from the real data compared
	to the same test statistics calculated from each of the random data
	sets. A drawback to this powerful technique is that each additional
	digit of p-value precision requires ten times as many replicated
	datasets, and the additional processing can lead to excessive run
	times.\&\#10;Results:\&\#10;We propose a new method for obtaining
	more precise p-values with a given number of replicates. The collection
	of test statistics from the random replicates is used to estimate
	the true distribution of the test statistic under the null hypothesis
	by fitting a continuous distribution to these observations. The choice
	of distribution is critical, and for the spatial and space-time scan
	statistics, the extreme value Gumbel distribution performs very well
	while the gamma, normal and lognormal distributions perform poorly.
	From the fitted Gumbel distribution, we show that it is possible
	to estimate the analytical p-value with great precision even when
	the test statistic is far out in the tail beyond any of the test
	statistics observed in the simulated replicates. In addition, Gumbel-based
	rejection probabilities have smaller variability than Monte Carlo-based
	rejection probabilities, suggesting that the proposed approach may
	result in greater power than the true Monte Carlo hypothesis test
	for a given number of replicates.\&\#10;Conclusions:\&\#10;For large
	data sets, it is often advantageous to replace computer intensive
	Monte Carlo hypothesis testing with this new method of fitting a
	Gumbel distribution to random data sets generated under the null,
	in order to reduce computation time and obtain much more precise
	p-values and slightly higher statistical power.}},
  citeulike-article-id = {8445794},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-9-61},
  day = {17},
  doi = {10.1186/1476-072X-9-61},
  issn = {1476-072X},
  keywords = {clustering, monte\_carlo, significance, space-time},
  posted-at = {2010-12-17 19:05:11},
  priority = {4},
  url = {http://dx.doi.org/10.1186/1476-072X-9-61}
}

@INPROCEEDINGS{citeulike:1833130,
  author = {Al-Khalifa, Hend S. and Davis, Hugh C.},
  title = {{Towards better understanding of folksonomic patterns}},
  booktitle = {HT '07: Proceedings of the 18th conference on Hypertext and hypermedia},
  year = {2007},
  pages = {163--166},
  address = {New York, NY, USA},
  publisher = {ACM},
  citeulike-article-id = {1833130},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1286288},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1286240.1286288},
  doi = {10.1145/1286240.1286288},
  isbn = {9781595938206},
  keywords = {clustering, folksonomy, semantic, tagging},
  posted-at = {2007-10-28 23:06:39},
  priority = {2},
  url = {http://dx.doi.org/10.1145/1286240.1286288}
}

@ARTICLE{citeulike:845228,
  author = {Aldstadt, J. and Getis, A.},
  title = {{Using AMOEBA to Create a Spatial Weights Matrix and Identify Spatial
	Clusters}},
  journal = {Geographical Analysis},
  year = {2006},
  volume = {38},
  pages = {327--343},
  number = {4},
  month = {October},
  abstract = {{The creation of a spatial weights matrix by a procedure called AMOEBA,
	A Multidirectional Optimum Ecotope-Based Algorithm, is dependent
	on the use of a local spatial autocorrelation statistic. The result
	is (1) a vector that identifies those spatial units that are related
	and unrelated to contiguous spatial units and (2) a matrix of weights
	whose values are a function of the relationship of the ith spatial
	unit with all other nearby spatial units for which there is a spatial
	association. In addition, the AMOEBA procedure aids in the demarcation
	of clusters, called ecotopes, of related spatial units. Experimentation
	reveals that AMOEBA is an effective tool for the identification of
	clusters. A comparison with a scan statistic procedure (SaTScan)
	gives evidence of the value of AMOEBA. Total fertility rates in enumeration
	districts in Amman, Jordan, are used to show a real-world example
	of the use of AMOEBA for the construction of a spatial weights matrix
	and for the identification of clusters. Again, comparisons reveal
	the effectiveness of the AMOEBA procedure.}},
  citeulike-article-id = {845228},
  citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1538-4632.2006.00689.x},
  citeulike-linkout-1 = {http://www.ingentaconnect.com/content/bsc/gean/2006/00000038/00000004/art00001},
  doi = {10.1111/j.1538-4632.2006.00689.x},
  issn = {0016-7363},
  keywords = {analysis, clustering, spatial},
  posted-at = {2010-09-18 22:55:11},
  priority = {3},
  publisher = {Blackwell Publishing Inc},
  url = {http://dx.doi.org/10.1111/j.1538-4632.2006.00689.x}
}

@ARTICLE{citeulike:1479823,
  author = {Ali, Mohammad and Jin, Yang and Kim, Deok R. and De, Zhou B. and
	Park, Jin K. and Ochiai, Rion L. and Dong, Baiqing and Clemens, John
	D. and Acosta, Camilo J.},
  title = {{Spatial risk for gender-specific adult mortality in an area of southern
	China}},
  journal = {International Journal of Health Geographics},
  year = {2007},
  volume = {6},
  pages = {31+},
  month = {July},
  citeulike-article-id = {1479823},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-6-31},
  day = {24},
  doi = {10.1186/1476-072X-6-31},
  issn = {1476-072X},
  keywords = {clustering, health, spatial, statistical},
  posted-at = {2007-07-25 19:44:54},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-072X-6-31}
}

@MISC{citeulike:1607149,
  author = {Allan, J. and Leouski, A. and Swan, R.},
  title = {{Interactive Cluster Visualization for Information Retrieval}},
  year = {1997},
  abstract = {{This study investigates the ability of cluster visualization to help
	a user rapidly identify relevant documents. It provides added support
	for the truth of the Cluster Hypothesis on retrieved documents and
	shows that clustering of relevant documents is readily visible. The
	study then shows the visual effect of a technique similar to relevance
	feedback and shows how to enhance that effect to further help the
	user locate relevant material.
	
	A ranked list returned by a text search engine purports...}},
  citeulike-article-id = {1607149},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.9195},
  keywords = {cluster, clustering, data-mining, document, information-retrieval,
	infovis, visualization},
  posted-at = {2007-08-30 14:21:49},
  priority = {2},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.9195}
}

@ARTICLE{Amento2003,
  author = {Amento, Brian and Terveen, Loren and Hill, Will and Hix, Deborah
	and Schulman, Robert},
  title = {{Experiments in social data mining: The TopicShop system}},
  journal = {ACM Trans. Comput.-Hum. Interact.},
  year = {2003},
  volume = {10},
  pages = {54--85},
  number = {1},
  month = {March},
  address = {New York, NY, USA},
  citeulike-article-id = {163220},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=606661},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/606658.606661},
  doi = {10.1145/606658.606661},
  issn = {1073-0516},
  keywords = {classification, cluster, collaborative, data-mining, delicious, filter,
	information-retrieval, social\_computing, social\_network, socialsoftware,
	tagging, taxonomy},
  owner = {ijt1},
  posted-at = {2006-05-11 22:49:20},
  priority = {3},
  publisher = {ACM Press},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1145/606658.606661}
}

@INCOLLECTION{citeulike:1450025,
  author = {Auer, S\"{o}ren and Lehmann, Jens},
  title = {{What Have Innsbruck and Leipzig in Common? Extracting Semantics
	from Wiki Content}},
  year = {2007},
  pages = {503--517},
  abstract = {{Wikis are established means for the collaborative authoring, versioning
	and publishing of textual articles. The Wikipedia project, for example,
	succeeded in creating the by far largest encyclopedia just on the
	basis of a wiki. Recently, several approaches have been proposed
	on how to extend wikis to allow the creation of structured and semantically
	enriched content. However, the means for creating semantically enriched
	structured content are already available and are, although unconsciously,
	even used by Wikipedia authors. In this article, we present a method
	for revealing this structured content by extracting information from
	template instances. We suggest ways to efficiently query the vast
	amount of extracted information (e.g. more than 8 million RDF statements
	for the English Wikipedia version alone), leading to astonishing
	query answering possibilities (such as for the title question). We
	analyze the quality of the extracted content, and propose strategies
	for quality improvements with just minor modifications of the wiki
	systems being currently used.}},
  citeulike-article-id = {1450025},
  citeulike-linkout-0 = {http://www.eswc2007.org/pdf/eswc07-auer.pdf},
  citeulike-linkout-1 = {http://dx.doi.org/10.1007/978-3-540-72667-8\_36},
  doi = {10.1007/978-3-540-72667-8\_36},
  journal = {The Semantic Web: Research and Applications},
  keywords = {automated, classification, clustering, data-mining, information-retrieval,
	knowledge-discovery, library, wiki},
  posted-at = {2007-07-12 19:59:08},
  priority = {4},
  url = {http://www.eswc2007.org/pdf/eswc07-auer.pdf}
}

@ARTICLE{Begelman2006,
  author = {Begelman, Grigory and Keller, Philipp and Smadja, Frank},
  title = {{Automated Tag Clustering: Improving search and exploration in the
	tag space}},
  year = {2006},
  citeulike-article-id = {941108},
  citeulike-linkout-0 = {http://www.pui.ch/phred/automated\_tag\_clustering/},
  keywords = {automated, clustering, exploration, folksonomy, information-retrieval,
	tagging},
  posted-at = {2007-01-16 20:10:39},
  priority = {0},
  url = {http://www.pui.ch/phred/automated\_tag\_clustering/}
}

@ARTICLE{citeulike:2905717,
  author = {Berry, M. and Browne, M. and Langville, A. and Pauca, V. and Plemmons,
	R.},
  title = {{Algorithms and applications for approximate nonnegative matrix factorization}},
  journal = {Computational Statistics \& Data Analysis},
  year = {2007},
  volume = {52},
  pages = {155--173},
  number = {1},
  month = {September},
  abstract = {{The development and use of low-rank approximate nonnegative matrix
	factorization (NMF) algorithms for feature extraction and identification
	in the fields of text mining and spectral data analysis are presented.
	The evolution and convergence properties of hybrid methods based
	on both sparsity and smoothness constraints for the resulting nonnegative
	matrix factors are discussed. The interpretability of NMF outputs
	in specific contexts are provided along with opportunities for future
	work in the modification of NMF algorithms for large-scale and time-varying
	data sets.}},
  citeulike-article-id = {2905717},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.csda.2006.11.006},
  citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0167947306004191},
  citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6V8V-4MFTTBR-2/1/3986651f267255ef5ab3426ff454aa6c},
  day = {15},
  doi = {10.1016/j.csda.2006.11.006},
  issn = {01679473},
  keywords = {algorithm, clustering, georss, rss},
  posted-at = {2008-06-18 16:27:31},
  priority = {2},
  url = {http://dx.doi.org/10.1016/j.csda.2006.11.006}
}

@ARTICLE{Besag1991,
  author = {Besag, Julian and Newell, James},
  title = {{The Detection of Clusters in Rare Diseases}},
  journal = {Journal of the Royal Statistical Society. Series A (Statistics in
	Society)},
  year = {1991},
  volume = {154},
  pages = {143--155},
  number = {1},
  abstract = {{Tests for clustering of rare diseases investigate whether an observed
	pattern of cases in one or more geographical regions could reasonably
	have arisen by chance alone, bearing in mind the variation in background
	population density. In contrast, tests for the detection of clusters
	are concerned with screening a large region for evidence of individual
	`hot spots' of disease but without any preconception about their
	likely locations; the results of such tests may form the basis for
	subsequent small area investigations, statistical or non-statistical,
	but will rarely be an end in themselves. The main intention of the
	paper is to describe and illustrate a new technique for the identification
	of small clusters of disease. A secondary purpose is to discuss some
	common pitfalls in the application of tests of clustering to epidemiological
	data.}},
  citeulike-article-id = {1463377},
  citeulike-linkout-0 = {http://dx.doi.org/10.2307/2982708},
  citeulike-linkout-1 = {http://www.jstor.org/stable/2982708},
  doi = {10.2307/2982708},
  keywords = {algorithm, automated, cancer, clustering, disease, spatial, spatial\_analysis},
  owner = {ijt1},
  posted-at = {2007-07-17 18:38:19},
  priority = {0},
  timestamp = {2011.01.19},
  url = {http://dx.doi.org/10.2307/2982708}
}

@ARTICLE{citeulike:674809,
  author = {Best, N. and Richardson, S. and Thomson, A.},
  title = {{A comparison of Bayesian spatial models for disease mapping.}},
  journal = {Stat Methods Med Res},
  year = {2005},
  volume = {14},
  pages = {35--59},
  number = {1},
  month = {February},
  abstract = {{With the advent of routine health data indexed at a fine geographical
	resolution, small area disease mapping studies have become an established
	technique in geographical epidemiology. The specific issues posed
	by the sparseness of the data and possibility for local spatial dependence
	belong to a generic class of statistical problems involving an underlying
	(latent) spatial process of interest corrupted by observational noise.
	These are naturally formulated within the framework of hierarchical
	models, and over the past decade, a variety of spatial models have
	been proposed for the latent level(s) of the hierarchy. In this article,
	we provide a comprehensive review of the main classes of such models
	that have been used for disease mapping within a Bayesian estimation
	paradigm, and report a performance comparison between representative
	models in these classes, using a set of simulated data to help illustrate
	their respective properties. We also consider recent extensions to
	model the joint spatial distribution of multiple disease or health
	indicators. The aim is to help the reader choose an appropriate structural
	prior for the second level of the hierarchical model and to discuss
	issues of sensitivity to this choice.}},
  address = {Small Area Health Statistics Unit, Department of Epidemiology and
	Public Health, Imperial College Faculty of Medicine, Norfolk Place,
	London W2 1PG, UK. n.best@ic.ac.uk},
  citeulike-article-id = {674809},
  citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/15690999},
  citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=15690999},
  issn = {0962-2802},
  keywords = {bayesian, clustering, disease, epidemiology, health, mapping, spatial},
  posted-at = {2008-10-21 16:27:06},
  priority = {2},
  url = {http://view.ncbi.nlm.nih.gov/pubmed/15690999}
}

@MASTERSTHESIS{citeulike:2316357,
  author = {Bhat, Samrat V.},
  title = {{Toxics Release Inventory facilities and childhood cancer: Geographic
	information systems based approach}},
  school = {THE UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH},
  year = {2007},
  abstract = {{Purpose. To examine the association between living in proximity to
	Toxics Release Inventory (TRI) facilities and the incidence of childhood
	cancer in the State of Texas.
	
	Design. This is a secondary data analysis utilizing the publicly available
	Toxics release inventory (TRI), maintained by the U.S. Environmental
	protection agency that lists the facilities that release any of the
	650 TRI chemicals. Total childhood cancer cases and childhood cancer
	rate (age 0-14 years) by county, for the years 1995-2003 were used
	from the Texas cancer registry, available at the Texas department
	of State Health Services website. Setting: This study was limited
	to the children population of the State of Texas.
	
	Method. Analysis was done using Stata version 9 and SPSS version 15.0.
	Satscan was used for geographical spatial clustering of childhood
	cancer cases based on county centroids using the Poisson clustering
	algorithm which adjusts for population density. Pictorial maps were
	created using MapInfo professional version 8.0.
	
	Results. One hundred and twenty five counties had no TRI facilities
	in their region, while 129 facilities had at least one TRI facility.
	An increasing trend for number of facilities and total disposal was
	observed except for the highest category based on cancer rate quartiles.
	Linear regression analysis using log transformation for number of
	facilities and total disposal in predicting cancer rates was computed,
	however both these variables were not found to be significant predictors.
	Seven significant geographical spatial clusters of counties for high
	childhood cancer rates (p<0.05) were indicated. Binomial logistic
	regression by categorizing the cancer rate in to two groups (<=150
	and >150) indicated an odds ratio of 1.58 (CI 1.127, 2.222) for the
	natural log of number of facilities.
	
	Conclusion. We have used a unique methodology by combining GIS and
	spatial clustering techniques with existing statistical approaches
	in examining the association between living in proximity to TRI facilities
	and the incidence of childhood cancer in the State of Texas. Although
	a concrete association was not indicated, further studies are required
	examining specific TRI chemicals. Use of this information can enable
	the researchers and public to identify potential concerns, gain a
	better understanding of potential risks, and work with industry and
	government to reduce toxic chemical use, disposal or other releases
	and the risks associated with them. TRI data, in conjunction with
	other information, can be used as a starting point in evaluating
	exposures and risks.}},
  citeulike-article-id = {2316357},
  citeulike-linkout-0 = {http://digitalcommons.library.tmc.edu/dissertations/AAI1445111/},
  keywords = {cancer, clustering, epidemiology, gis, health},
  posted-at = {2008-01-31 20:45:25},
  priority = {2},
  url = {http://digitalcommons.library.tmc.edu/dissertations/AAI1445111/}
}

@ARTICLE{citeulike:3024695,
  author = {Bivand, Roger and M\"{u}ller, Werner G. and Reder, Markus},
  title = {{Power Calculations for Global and Local Moran's}},
  journal = {Computational Statistics \& Data Analysis},
  volume = {In Press, Accepted Manuscript},
  citeulike-article-id = {3024695},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.csda.2008.07.021},
  citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0167947308003551},
  citeulike-linkout-2 = {http://www.sciencedirect.com/science/article/B6V8V-4T1FWFG-2/1/38e821b91bd7599865182a3171475ad4},
  doi = {10.1016/j.csda.2008.07.021},
  keywords = {clustering, gis, spatial, spatial\_analysis, statistical},
  posted-at = {2008-07-21 16:26:05},
  priority = {3},
  url = {http://dx.doi.org/10.1016/j.csda.2008.07.021}
}

@ARTICLE{citeulike:6740911,
  author = {Boscoe, F.},
  title = {{Visualization of the spatial scan statistic using nested circles}},
  journal = {Health \& Place},
  year = {2003},
  volume = {9},
  pages = {273--277},
  number = {3},
  month = {September},
  abstract = {{We propose a technique for the display of results of Kulldorff's
	spatial scan statistic and related cluster detection methods that
	provides a greater degree of informational content. By simultaneously
	considering likelihood ratio and relative risk, it is possible to
	identify focused sub-clusters of higher (or lower) relative risk
	among broader regional excesses or deficits. The result is a map
	with a nested or contoured appearance. Here the technique is applied
	to prostate cancer mortality data in counties within the contiguous
	United States during the period 1970â€“1994. The resulting map shows
	both broad and localized patterns of excess and deficit, which complements
	a choropleth map of the same data.}},
  citeulike-article-id = {6740911},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/S1353-8292(02)00060-6},
  citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1353-8292(02)00060-6},
  doi = {10.1016/S1353-8292(02)00060-6},
  issn = {13538292},
  keywords = {cluster, clustering, disease, health, spa, spatial\_analysis},
  posted-at = {2010-02-27 19:38:48},
  priority = {4},
  url = {http://dx.doi.org/10.1016/S1353-8292(02)00060-6}
}

@INPROCEEDINGS{citeulike:1371121,
  author = {Breaux, Travis D. and Reed, Joel W.},
  title = {{Using Ontology in Hierarchical Information Clustering}},
  booktitle = {HICSS '05: Proceedings of the Proceedings of the 38th Annual Hawaii
	International Conference on System Sciences (HICSS'05) - Track 4},
  year = {2005},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  abstract = {{The tools to analyze and visualize information from multiple, heterogeneous
	sources have often relied on innovations in statistical methods.
	The results from purely statistical methods, however, overlook relevant
	semantic features present within natural language and text-based
	information. Emerging research in ontology languages (e.g. RDF, RDFS,
	SUO-KIF, and OWL) offers promising avenues for overcoming these limitations
	by leveraging existing and future libraries of meta-data and semantic
	mark-up. Using semantic features (e.g. hypernyms, meronyms, synonyms,
	etc.) encoded in ontology languages, methods such as keyword search
	and clustering can be augmented to analyze and visualize documents
	at conceptually richer levels. We present findings from a hierarchical
	clustering system modified for ontological indexing and run on a
	topic-centric test collection of documents each with fewer than 200
	words. Our findings show that ontologies can impose a complete interpretation
	or subjective clustering onto a document set that is at least as
	good as meta-word search.}},
  citeulike-article-id = {1371121},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1042435.1042921},
  citeulike-linkout-1 = {http://dx.doi.org/10.1109/HICSS.2005.664},
  doi = {10.1109/HICSS.2005.664},
  keywords = {clustering, data-mining, information-retrieval, knowledge-discovery,
	ontology},
  posted-at = {2007-06-08 14:55:43},
  priority = {3},
  url = {http://dx.doi.org/10.1109/HICSS.2005.664}
}

@ARTICLE{citeulike:820226,
  author = {Brownstein, J. S. and Rosen, H. and Purdy, D. and Miller, J. R. and
	Merlino, M. and Mostashari, F. and Fish, D.},
  title = {{Spatial analysis of West Nile virus: rapid risk assessment of an
	introduced vector-borne zoonosis.}},
  journal = {Vector Borne Zoonotic Dis},
  year = {2002},
  volume = {2},
  pages = {157--164},
  number = {3},
  abstract = {{The distribution of human risk for West Nile virus was determined
	by spatial analysis of the initial case distribution for the New
	York City area in 1999 using remote sensing and geographic information
	system technologies. Cluster analysis revealed the presence of a
	statistically significant grouping of cases, which also indicates
	the area of probable virus introduction. Within the cluster, habitat
	suitability for potentially infective adult mosquitoes was measured
	by the amount of vegetation cover using satellite imagery. Logistic
	regression analysis revealed satellite-derived vegetation abundance
	to be significantly and positively associated with the presence of
	human cases. The logistic model was used to estimate the spatial
	distribution of human risk for West Nile virus throughout New York
	City. Accuracy of the resulting risk map was cross-validated using
	virus-positive mosquito sample sites. These new epidemiological methods
	aid in rapid entry point identification and spatial prediction of
	human risk of infection for introduced vector-borne pathogens.}},
  address = {Department of Epidemiology and Public Health, Yale School of Medicine,
	New Haven, Connecticut 06520-8034, USA.},
  citeulike-article-id = {820226},
  citeulike-linkout-0 = {http://dx.doi.org/10.1089/15303660260613729},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/12737545},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=12737545},
  doi = {10.1089/15303660260613729},
  issn = {1530-3667},
  keywords = {clustering, geographic, geospatial, gis, health, westnilevirus},
  posted-at = {2006-08-28 21:44:16},
  priority = {2},
  url = {http://dx.doi.org/10.1089/15303660260613729}
}

@ARTICLE{citeulike:2879543,
  author = {Capocci, Andrea and Caldarelli, Guido},
  title = {{Folksonomies and clustering in the collaborative system CiteULike}},
  journal = {Journal of Physics A: Mathematical and Theoretical},
  year = {2008},
  volume = {41},
  pages = {224016+},
  number = {22},
  month = {June},
  abstract = {{We analyze CiteULike , an online collaborative tagging system where
	users bookmark and annotate scientific papers. Such a system can
	be naturally represented as a tri-partite graph whose nodes represent
	papers, users and tags connected by individual tag assignments. The
	semantics of tags is studied here, in order to uncover the hidden
	relationships between tags. We find that the clustering coefficient
	can be used to analyze the semantical patterns among tags.}},
  citeulike-article-id = {2879543},
  citeulike-linkout-0 = {http://dx.doi.org/10.1088/1751-8113/41/22/224016},
  citeulike-linkout-1 = {http://iopscience.iop.org/1751-8113/41/22/224016},
  day = {06},
  doi = {10.1088/1751-8113/41/22/224016},
  issn = {1751-8113},
  keywords = {citations, clustering, collaborative, folksonomy, tagging},
  posted-at = {2008-06-10 15:55:01},
  priority = {4},
  url = {http://dx.doi.org/10.1088/1751-8113/41/22/224016}
}

@ARTICLE{Chen2006,
  author = {Chen, Chaomei},
  title = {{CiteSpace II: Detecting and visualizing emerging trends and transient
	patterns in scientific literature}},
  journal = {Journal of the American Society for Information Science and Technology},
  year = {2006},
  volume = {57},
  pages = {359--377},
  number = {3},
  month = {February},
  abstract = {{This article describes the latest development of a generic approach
	to detecting and visualizing emerging trends and transient patterns
	in scientific literature. The work makes substantial theoretical
	and methodological contributions to progressive knowledge domain
	visualization. A specialty is conceptualized and visualized as a
	time-variant duality between two fundamental concepts in information
	science: research fronts and intellectual bases. A research front
	is defined as an emergent and transient grouping of concepts and
	underlying research issues. The intellectual base of a research front
	is its citation and co-citation footprint in scientific literature - an
	evolving network of scientific publications cited by research-front
	concepts. Kleinberg's (2002) burst-detection algorithm is adapted
	to identify emergent research-front concepts. Freeman's (1979) betweenness
	centrality metric is used to highlight potential pivotal points of
	paradigm shift over time. Two complementary visualization views are
	designed and implemented: cluster views and time-zone views. The
	contributions of the approach are that (a) the nature of an intellectual
	base is algorithmically and temporally identified by emergent research-front
	terms, (b) the value of a co-citation cluster is explicitly interpreted
	in terms of research-front concepts, and (c) visually prominent and
	algorithmically detected pivotal points substantially reduce the
	complexity of a visualized network. The modeling and visualization
	process is implemented in CiteSpace II, a Java application, and applied
	to the analysis of two research fields: mass extinction (1981-2004)
	and terrorism (1990-2003). Prominent trends and pivotal points in
	visualized networks were verified in collaboration with domain experts,
	who are the authors of pivotal-point articles. Practical implications
	of the work are discussed. A number of challenges and opportunities
	for future studies are identified.}},
  address = {Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104-2875},
  citeulike-article-id = {1044587},
  citeulike-linkout-0 = {http://dx.doi.org/10.1002/asi.20317},
  citeulike-linkout-1 = {http://www3.interscience.wiley.com/cgi-bin/abstract/112215896/ABSTRACT},
  day = {01},
  doi = {10.1002/asi.20317},
  issn = {15322882},
  keywords = {analysis, citations, cluster, time-series, visualization},
  owner = {ijt1},
  posted-at = {2007-01-16 19:43:55},
  priority = {3},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1002/asi.20317}
}

@ARTICLE{Chen2008,
  author = {Chen, Jin and Roth, Robert E. and Naito, Adam T. and Lengerich, Eugene
	J. and Maceachren, Alan M.},
  title = {Geovisual analytics to enhance spatial scan statistic interpretation:
	an analysis of {U.S.} cervical cancer mortality},
  journal = {International Journal of Health Geographics},
  year = {2008},
  volume = {7},
  pages = {57+},
  month = {November},
  citeulike-article-id = {3495205},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-7-57},
  day = {07},
  doi = {10.1186/1476-072X-7-57},
  issn = {1476-072X},
  keywords = {cancer, cluster, geography, geovisualization, health, statscan},
  owner = {ijt1},
  posted-at = {2008-11-09 22:22:46},
  priority = {4},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1186/1476-072X-7-57}
}

@INPROCEEDINGS{citeulike:600787,
  author = {Cherng, Jong-Sheng and Lo, Mei-Jung},
  title = {{A Hypergraph Based Clustering Algorithm for Spatial Data Sets}},
  booktitle = {ICDM '01: Proceedings of the 2001 IEEE International Conference on
	Data Mining},
  year = {2001},
  pages = {83--90},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  citeulike-article-id = {600787},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=645496.658031},
  isbn = {0769511198},
  keywords = {clustering, space, spatial},
  posted-at = {2006-04-26 01:57:44},
  priority = {2},
  url = {http://portal.acm.org/citation.cfm?id=645496.658031}
}

@ARTICLE{Cilibrasi2007,
  author = {Cilibrasi, Rudi L. and Vitanyi, Paul M. B.},
  title = {{The Google Similarity Distance}},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2007},
  volume = {19},
  pages = {370--383},
  number = {3},
  month = {March},
  abstract = {{Words and phrases acquire meaning from the way they are used in society,
	from their relative semantics to other words and phrases. For computers,
	the equivalent of "society" is "database," and the equivalent of
	"use" is "a way to search the database." We present a new theory
	of similarity between words and phrases based on information distance
	and Kolmogorov complexity. To fix thoughts, we use the World Wide
	Web (WWW) as the database, and Google as the search engine. The method
	is also applicable to other search engines and databases. This theory
	is then applied to construct a method to automatically extract similarity,
	the Google similarity distance, of words and phrases from the WWW
	using Google page counts. The WWW is the largest database on earth,
	and the context information entered by millions of independent users
	averages out to provide automatic semantics of useful quality. We
	give applications in hierarchical clustering, classification, and
	language translation. We give examples to distinguish between colors
	and numbers, cluster names of paintings by 17th century Dutch masters
	and names of books by English novelists, the ability to understand
	emergencies and primes, and we demonstrate the ability to do a simple
	automatic English-Spanish translation. Finally, we use the WordNet
	database as an objective baseline against which to judge the performance
	of our method. We conduct a massive randomized trial in binary classification
	using support vector machines to learn categories based on our Google
	distance, resulting in an a mean agreement of 87 percent with the
	expert crafted WordNet categories.}},
  address = {Los Alamitos, CA, USA},
  citeulike-article-id = {1717609},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1263333},
  citeulike-linkout-1 = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2007.48},
  citeulike-linkout-2 = {http://dblp.uni-trier.de/rec/bibtex/journals/tkde/CilibrasiV07},
  citeulike-linkout-3 = {http://dx.doi.org/10.1109/TKDE.2007.48},
  citeulike-linkout-4 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4072748},
  doi = {10.1109/TKDE.2007.48},
  issn = {1041-4347},
  keywords = {classification, cluster, google, information-retrieval, knowledge-discovery},
  owner = {ijt1},
  posted-at = {2007-10-02 19:25:26},
  priority = {2},
  publisher = {IEEE Computer Society},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1109/TKDE.2007.48}
}

@ARTICLE{citeulike:1463340,
  author = {Congdon, Peter},
  title = {{Mixtures of spatial and unstructured effects for spatially discontinuous
	health outcomes}},
  journal = {Computational Statistics \& Data Analysis},
  year = {2007},
  volume = {51},
  pages = {3197--3212},
  number = {6},
  month = {March},
  abstract = {{Mixture models are used for spatially adaptive smoothing of health
	event data (e.g. mortality or illness totals). Such models allow
	for spatial pooling of strength where appropriate but adopt a mixture
	strategy that also reflects health risks that are discordant with
	those of surrounding areas. Mixing is either discrete or based on
	beta densities. A fully Bayesian estimation and specification strategy
	is applied with fit based on DIC and BIC criteria. Illustrative applications
	are to long term illness in 133 London small areas, where event counts
	are large, and to lip cancer in Scottish counties where the majority
	of event totals are under 10.}},
  citeulike-article-id = {1463340},
  citeulike-linkout-0 = {http://www.sciencedirect.com/science/article/B6V8V-4MJSF46-2/2/3e32bfc60b6d501f65782a0151c8e55f},
  day = {1},
  keywords = {bayesian, cancer, clustering, disease, maps, modeling, spatial\_analysis},
  posted-at = {2007-07-17 18:31:02},
  priority = {2},
  url = {http://www.sciencedirect.com/science/article/B6V8V-4MJSF46-2/2/3e32bfc60b6d501f65782a0151c8e55f}
}

@ARTICLE{Conley2005,
  author = {Conley, Jamison and Gahegan, Mark and Macgill, James},
  title = {{A Genetic Approach to Detecting Clusters in Point Data Sets}},
  journal = {Geographical Analysis},
  year = {2005},
  volume = {37},
  pages = {286--314},
  number = {3},
  abstract = {{Spatial analysis techniques are widely used throughout geography.
	However, as the size of geographic data sets increases exponentially,
	limitations to the traditional methods of spatial analysis become
	apparent. To overcome some of these limitations, many algorithms
	for exploratory spatial analysis have been developed. This article
	presents both a new cluster detection method based on a genetic algorithm,
	and Programs for Cluster Detection, a toolkit application containing
	the new method as well as implementations of three established methods:
	Openshaw's Geographical Analysis Machine (GAM), case point-centered
	searching (proposed by Besag and Newell), and randomized GAM (proposed
	by Fotheringham and Zhan). We compare the effectiveness of cluster
	detection and the runtime performance of these four methods and Kulldorf's
	spatial scan statistic on a synthetic point data set simulating incidence
	of a rare disease among a spatially variable background population.
	The proposed method has faster average running times than the other
	methods and significantly reduces overreporting of the underlying
	clusters, thus reducing the user's postprocessing burden. Therefore,
	the proposed method improves upon previous methods for automated
	cluster detection. The results of our method are also compared with
	those of Map Explorer (MAPEX), a previous attempt to develop a genetic
	algorithm for cluster detection. The results of these comparisons
	indicate that our method overcomes many of the problems faced by
	MAPEX, thus, we believe, establishing that genetic algorithms can
	indeed offer a viable approach to cluster detection.}},
  citeulike-article-id = {2465761},
  citeulike-linkout-0 = {http://www.blackwell-synergy.com/doi/abs/10.1111/j.1538-4632.2005.00617.x},
  citeulike-linkout-1 = {http://dx.doi.org/10.1111/j.1538-4632.2005.00617.x},
  doi = {10.1111/j.1538-4632.2005.00617.x},
  keywords = {cluster, disease, epidemiology, gam, geneticalgorithms, geography,
	health},
  owner = {ijt1},
  posted-at = {2008-03-04 16:59:57},
  priority = {3},
  timestamp = {2011.01.19},
  url = {http://dx.doi.org/10.1111/j.1538-4632.2005.00617.x}
}

@ARTICLE{Cucala2008,
  author = {Cucala, L.},
  title = {{A flexible spatial scan test for case event data}},
  journal = {Computational Statistics \& Data Analysis},
  year = {2008},
  month = {October},
  citeulike-article-id = {3431769},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.csda.2008.10.008},
  citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0167947308004714},
  day = {17},
  doi = {10.1016/j.csda.2008.10.008},
  issn = {01679473},
  keywords = {cluster, health, spatial},
  owner = {ijt1},
  posted-at = {2008-10-20 22:21:05},
  priority = {2},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1016/j.csda.2008.10.008}
}

@ARTICLE{Demattei2007,
  author = {Demattei, Christophe and Molinari, Nicolas and Daures, Jean-Pierre},
  title = {{Arbitrarily shaped multiple spatial cluster detection for case event
	data}},
  journal = {Computational Statistics \& Data Analysis},
  year = {2007},
  volume = {51},
  pages = {3931--3945},
  number = {8},
  month = {May},
  abstract = {{An original method is proposed for spatial cluster detection of case
	event data. A selection order and the distance from the nearest neighbour
	are attributed to each point, once pre-selected points have been
	taken into account. This distance is weighted by the expected distance
	under the uniform distribution hypothesis. Potential clusters are
	located by modelling the multiple structural change of the distances
	on the selection order and the best model (containing one or several
	potential clusters) is selected using the double maximum test. Finally
	a p-value is obtained for each potential cluster. With this method
	multiple clusters of any shape can be detected.}},
  citeulike-article-id = {1463324},
  citeulike-linkout-0 = {http://www.sciencedirect.com/science/article/B6V8V-4JT9KF4-1/2/41663bd252c2fa98e6ac8a413e31222e},
  day = {1},
  keywords = {cluster, methodologies, networks, space, spatial, spatial\_analysis},
  owner = {ijt1},
  posted-at = {2007-07-17 18:16:53},
  priority = {3},
  timestamp = {2011.01.20},
  url = {http://www.sciencedirect.com/science/article/B6V8V-4JT9KF4-1/2/41663bd252c2fa98e6ac8a413e31222e}
}

@ARTICLE{citeulike:1836287,
  author = {Di Giacomo, E. and Didimo, W. and Grilli, L. and Liotta, G.},
  title = {{Graph Visualization Techniques for Web Clustering Engines}},
  journal = {Transactions on Visualization and Computer Graphics},
  year = {2007},
  volume = {13},
  pages = {294--304},
  number = {2},
  abstract = {{One of the most challenging issues in mining information from the
	World Wide Web is the design of systems that present the data to
	the end user by clustering them into meaningful semantic categories.
	We show that the analysis of the results of a clustering engine can
	significantly take advantage of enhanced graph drawing and visualization
	techniques. We propose a graph-based user interface for Web clustering
	engines that makes it possible for the user to explore and visualize
	the different semantic categories and their relationships at the
	desired level of detail}},
  booktitle = {Transactions on Visualization and Computer Graphics},
  citeulike-article-id = {1836287},
  citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4069238},
  keywords = {clustering, graph, visualization, web},
  posted-at = {2007-10-29 17:23:16},
  priority = {3},
  url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4069238}
}

@ARTICLE{citeulike:7298406,
  author = {Du, Ping and Lemkin, Allison and Kluhsman, Brenda and Chen, Jin and
	Roth, Robert and MacEachren, Alan and Meyers, Craig and Zurlo, John
	and Lengerich, Eugene},
  title = {{The roles of social domains, behavioral risk, health care resources,
	and chlamydia in spatial clusters of US cervical cancer mortality:
	not all the clusters are the same}},
  journal = {Cancer Causes and Control},
  year = {2010},
  volume = {21},
  pages = {1669-1683--1683},
  number = {10},
  month = {October},
  abstract = {{Background\^{A}Â \^{A}Â While high-risk geographic clusters of cervical
	cancer mortality have previously been assessed, factors associated
	with this geographic patterning have not been well studied. Once
	these factors are identified, etiologic hypotheses and targeted population-based
	interventions may be developed and lead to a reduction in geographic
	disparities in cervical cancer mortality. Methods\^{A}Â \^{A}Â The
	authors linked multiple data sets at the county level to assess the
	effects of social domains, behavioral risk factors, local physician
	and hospital availability, and Chlamydia trachomatis infection on
	overall spatial clustering and on individual clusters of cervical
	cancer mortality rates in 2000\^{a}Â€Â“2004 among 3,105 US counties
	in the 48 states and the District of Columbia. Results\^{A}Â \^{A}Â During
	the study period, a total of 19,898 cervical cancer deaths occurred
	in women aged 20 and older. The distributions of county-level characteristics
	indicated wide ranges in social domains measured by demographics
	and socioeconomic status, local health care resources, and the rate
	of chlamydial infection. We found that overall geographic clustering
	of increased cervical cancer mortality was related to the high proportion
	of black population, low socioeconomic status, low Papanicolaou test
	rate, low health care coverage, and the high chlamydia rate; however,
	unique characteristics existed for each individual cluster, and the
	Appalachian cluster was not related to a high proportion of black
	population or to chlamydia rates. Discussion\^{A}Â \^{A}Â This study
	indicates that local social domains, behavioral risk, and health
	care sources are associated with geographic disparities in cervical
	cancer mortality rates. The association between the chlamydia rate
	and the cervical cancer mortality rate may be confounded by other
	factors known to be a risk for cervical cancer mortality, such as
	the infection with human papillomavirus. The findings will help cancer
	researchers examine etiologic hypotheses and develop tailored, cluster-specific
	interventions to reduce cervical cancer disparities.}},
  citeulike-article-id = {7298406},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10552-010-9596-4},
  citeulike-linkout-1 = {http://www.springerlink.com/content/638286k176223g24},
  day = {1},
  doi = {10.1007/s10552-010-9596-4},
  issn = {0957-5243},
  keywords = {cancer, clustering, health, spatial\_analysis},
  posted-at = {2010-09-22 18:17:32},
  priority = {2},
  publisher = {Springer Netherlands},
  url = {http://dx.doi.org/10.1007/s10552-010-9596-4}
}

@ARTICLE{citeulike:4185066,
  author = {Duczmal, L.},
  title = {{A simulated annealing strategy for the detection of arbitrarily
	shaped spatial clusters}},
  journal = {Computational Statistics \& Data Analysis},
  year = {2004},
  volume = {45},
  pages = {269--286},
  number = {2},
  month = {March},
  abstract = {{We propose a new graph-based strategy for the detection of spatial
	clusters of arbitrary geometric form in a map of geo-referenced populations
	and cases. Our test statistic is based on the likelihood ratio test
	previously formulated by Kulldorff and Nagarwalla for circular clusters.
	A new technique of adaptive simulated annealing is developed, focused
	on the problem of finding the local maxima of a certain likelihood
	function over the space of the connected subgraphs of the graph associated
	to the regions of interest. Given a map with n regions, on average
	this algorithm finds a quasi-optimal solution after analyzing sn
	log( n ) subgraphs, where s depends on the cases density uniformity
	in the map. The algorithm is applied to a study of homicide clusters
	detection in a Brazilian large metropolitan area.}},
  citeulike-article-id = {4185066},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.8840\&\#38;rep=rep1\&\#38;type=pdf},
  citeulike-linkout-1 = {http://dx.doi.org/10.1016/S0167-9473(02)00302-X},
  citeulike-linkout-2 = {http://linkinghub.elsevier.com/retrieve/pii/S0167-9473(02)00302-X},
  day = {01},
  doi = {10.1016/S0167-9473(02)00302-X},
  issn = {01679473},
  keywords = {clustering, geography, spatial},
  posted-at = {2009-03-17 13:26:47},
  priority = {4},
  url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.8840\&\#38;rep=rep1\&\#38;type=pdf}
}

@INPROCEEDINGS{citeulike:221046,
  author = {Eades, Peter and Feng, Qing-Wen},
  title = {{Multilevel Visualization of Clustered Graphs}},
  booktitle = {Proc. Graph Drawing, GD},
  year = {1996},
  number = {1190},
  pages = {101--112},
  address = {Berlin, Germany},
  month = {JanuaryAugust--February0\~{}},
  publisher = {Springer-Verlag},
  abstract = {{Clustered graphs are graphs with recursive clustering structures
	over the vertices. This type of structure appears in many systems.
	Examples include CASE tools, management information systems, VLSI
	design tools, and reverse engineering systems. Existing layout algorithms
	represent the clustering structure as recursively nested regions
	in the plane. However, as the structure becomes more and more complex,
	two dimensional plane representations tend to be insufficient. In
	this paper, firstly, we...}},
  citeulike-article-id = {221046},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.6760},
  keywords = {clustering, graphs, multilevel, visualization},
  posted-at = {2006-04-26 02:03:56},
  priority = {2},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.49.6760}
}

@ARTICLE{citeulike:8322654,
  author = {Fisher, Walter D.},
  title = {{On Grouping for Maximum Homogeneity}},
  journal = {Journal of the American Statistical Association},
  year = {1958},
  volume = {53},
  number = {284},
  abstract = {{Given a set of arbitrary numbers, what is a practical procedure for
	grouping them so that the variance within groups is minimized? An
	answer to this question, including a description of an automatic
	computer program, is given for problems up to the size where 200
	numbers are to be placed in 10 groups. Two basic types of problem
	are discussed and illustrated.}},
  citeulike-article-id = {8322654},
  citeulike-linkout-0 = {http://dx.doi.org/10.2307/2281952},
  citeulike-linkout-1 = {http://www.jstor.org/stable/2281952},
  comment = {The mathematical basis of all the algorithms to calculate Jenks' Natural
	Breaks},
  doi = {10.2307/2281952},
  issn = {01621459},
  keywords = {classification, clustering},
  posted-at = {2010-11-29 20:34:07},
  priority = {0},
  publisher = {American Statistical Association},
  url = {http://dx.doi.org/10.2307/2281952}
}

@INCOLLECTION{citeulike:2210063,
  author = {Flexer, Arthur},
  title = {{On the Use of Self-Organizing Maps for Clustering and Visualization}},
  year = {1999},
  pages = {80--88},
  abstract = {{We show that the number of output units used in a self-organizing
	map (SOM) influences its applicability for either clustering or visualization.
	By reviewing the appropriate literature and theory and own empirical
	results, we demonstrate that SOMs can be used for clustering or visualization
	separately, for simultaneous clustering and visualization, and even
	for clustering via visualization. For all these different kinds of
	application, SOM is compared to other statistical approaches. This
	will show SOM to be a flexible tool which can be used for various
	forms of explorative data analysis but it will also be made obvious
	that this flexibility comes with a price in terms of impaired performance.
	The usage of SOM in the data mining community is covered by discussing
	its application in the data mining tools CLEMENTINE and WEBSOM.}},
  citeulike-article-id = {2210063},
  citeulike-linkout-0 = {http://www.springerlink.com/content/30ul27kr2gff0l92},
  journal = {Principles of Data Mining and Knowledge Discovery},
  keywords = {clustering, som, visualization},
  posted-at = {2008-01-10 19:14:47},
  priority = {3},
  url = {http://www.springerlink.com/content/30ul27kr2gff0l92}
}

@ARTICLE{citeulike:8653754,
  author = {Fotheringham, A. Stewart and Zhan, F. Benjamin},
  title = {{A Comparison of Three Exploratory Methods for Cluster Detection
	in Spatial Point Patterns}},
  journal = {Geographical Analysis},
  year = {1996},
  volume = {28},
  pages = {200--218},
  number = {3},
  abstract = {{This paper compares the performances of three exploratory methods
	for cluster detection in spatial point patterns where the at-risk
	population is known. After reviewing two existing methods, Openshaw
	et al. (1987) and Besag and Newell (1991), an alternative method
	is introduced. These three methods are then compared empirically
	using two point patterns drawn from a disaggregate housing database
	consisting of 28,832 observations. Each observation in the data set
	contains attributes of single-family detached dwellings in the City
	of Amherst, New York. This paper provides some new insights into
	the performance of the three methods, as previous applications have
	used spatially aggregated (and hence rather inaccurate) data. The
	paper also demonstrates the utility of GIS for this type of spatial
	analysis.}},
  citeulike-article-id = {8653754},
  citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1538-4632.1996.tb00931.x},
  doi = {10.1111/j.1538-4632.1996.tb00931.x},
  keywords = {cluster\_detection, clustering, gam, health},
  owner = {ijt1},
  posted-at = {2011-01-20 00:01:15},
  priority = {0},
  publisher = {Blackwell Publishing Ltd},
  timestamp = {2011.01.19},
  url = {http://dx.doi.org/10.1111/j.1538-4632.1996.tb00931.x}
}

@TECHREPORT{citeulike:1469898,
  author = {Freeman, Hp and Wingrove, Bk},
  title = {{Excess Cervical Cancer Mortality A Marker for Low Access to Health
	Care in Poor Communities}},
  institution = {National Cancer Institute, Center to Reduce Cancer Health},
  year = {2005},
  address = {Rockville, MD},
  month = {May},
  citeulike-article-id = {1469898},
  citeulike-linkout-0 = {http://crchd.cancer.gov/attachments/excess-cervcanmort.pdf},
  keywords = {analysis, cancer, clustering, epidemiology, geospatial, socioeconomic,
	space, space-time, statistical},
  posted-at = {2007-07-20 16:49:34},
  priority = {4},
  url = {http://crchd.cancer.gov/attachments/excess-cervcanmort.pdf},
  volume = {NIH Pub. No. 05â€“5282}
}

@ARTICLE{citeulike:1051630,
  author = {Frey, Brendan J. and Dueck, Delbert},
  title = {{Clustering by Passing Messages Between Data Points}},
  journal = {Science},
  year = {2007},
  volume = {315},
  pages = {972--976},
  number = {5814},
  month = {February},
  abstract = {{Clustering data by identifying a subset of representative examples
	is important for processing sensory signals and detecting patterns
	in data. Such â€� exemplarsâ€� can be found by randomly choosing
	an initial subset of data points and then iteratively refining it,
	but this works well only if that initial choice is close to a good
	solution. We devised a method called â€� affinity propagation,â€�
	which takes as input measures of similarity between pairs of data
	points. Real-valued messages are exchanged between data points until
	a high-quality set of exemplars and corresponding clusters gradually
	emerges. We used affinity propagation to cluster images of faces,
	detect genes in microarray data, identify representative sentences
	in this manuscript, and identify cities that are efficiently accessed
	by airline travel. Affinity propagation found clusters with much
	lower error than other methods, and it did so in less than one-hundredth
	the amount of time.}},
  address = {Department of Electrical and Computer Engineering, University of
	Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada.},
  citeulike-article-id = {1051630},
  citeulike-linkout-0 = {http://dx.doi.org/10.1126/science.1136800},
  citeulike-linkout-1 = {http://www.sciencemag.org/content/315/5814/972.abstract},
  citeulike-linkout-2 = {http://www.sciencemag.org/content/315/5814/972.full.pdf},
  citeulike-linkout-3 = {http://www.sciencemag.org/cgi/content/abstract/315/5814/972},
  citeulike-linkout-4 = {http://adsabs.harvard.edu/cgi-bin/nph-bib\_query?bibcode=2007Sci...315..972F},
  citeulike-linkout-5 = {http://view.ncbi.nlm.nih.gov/pubmed/17218491},
  citeulike-linkout-6 = {http://www.hubmed.org/display.cgi?uids=17218491},
  day = {16},
  doi = {10.1126/science.1136800},
  issn = {1095-9203},
  keywords = {algorithm, cluster, clustering, data},
  posted-at = {2007-11-13 22:53:50},
  priority = {3},
  url = {http://dx.doi.org/10.1126/science.1136800}
}

@ARTICLE{citeulike:956061,
  author = {Gaudart, Jean and Poudiougou, Belco and Dicko, Alassane and Ranque,
	Stephane and Toure, Ousmane and Sagara, Issaka and Diallo, Mouctar
	and Diawara, Sory and Ouattara, Amed and Diakite, Mahamadou and Doumbo,
	Ogobara K.},
  title = {{Space-time clustering of childhood malaria at the household level:
	a dynamic cohort in a Mali village}},
  journal = {BMC Public Health},
  year = {2006},
  volume = {6},
  pages = {286+},
  month = {November},
  citeulike-article-id = {956061},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2458-6-286},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17118176},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17118176},
  day = {21},
  doi = {10.1186/1471-2458-6-286},
  issn = {1471-2458},
  keywords = {clustering, epidemiology, gam, health},
  posted-at = {2008-02-07 14:27:55},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1471-2458-6-286}
}

@ARTICLE{citeulike:969662,
  author = {Goovaerts, Pierre},
  title = {{Geostatistical analysis of disease data: accounting for spatial
	support and population density in the isopleth mapping of cancer
	mortality risk using area-to-point Poisson kriging}},
  journal = {International Journal of Health Geographics},
  year = {2006},
  volume = {5},
  pages = {52+},
  month = {November},
  citeulike-article-id = {969662},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-5-52},
  day = {30},
  doi = {10.1186/1476-072X-5-52},
  issn = {1476-072X},
  keywords = {clustering, disease, epidemiology, geography, geospatial, spatial\_analysis,
	statistical},
  posted-at = {2006-12-01 14:44:03},
  priority = {4},
  url = {http://dx.doi.org/10.1186/1476-072X-5-52}
}

@ARTICLE{Goovaerts2007,
  author = {Goovaerts, Pierre and Meliker, Jaymie R. and Jacquez, Geoffrey M.},
  title = {{A comparative analysis of aspatial statistics for detecting racial
	disparities in cancer mortality rates}},
  journal = {International Journal of Health Geographics},
  year = {2007},
  volume = {6},
  pages = {32+},
  month = {July},
  citeulike-article-id = {1479824},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-6-32},
  day = {24},
  doi = {10.1186/1476-072X-6-32},
  issn = {1476-072X},
  keywords = {cancer, cluster, disease, spatial, testing},
  owner = {ijt1},
  posted-at = {2007-07-25 19:42:42},
  priority = {2},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1186/1476-072X-6-32}
}

@ARTICLE{citeulike:4692686,
  author = {Goria, Sarah and Daniau, Come and de Crouy-Chanel, Perrine and Empereur-Bissonnet,
	Pascal and Fabre, Pascal and Colonna, Marc and Duboudin, Cedric and
	Viel, Jean-Francois and Richardson, Sylvia},
  title = {{Risk of cancer in the vicinity of municipal solid waste incinerators:
	importance of using a flexible modelling strategy}},
  journal = {International Journal of Health Geographics},
  year = {2009},
  volume = {8},
  pages = {31+},
  month = {May},
  abstract = {{Background:\&\#10;We conducted an ecological study in four French
	administrative departments and highlighted an excess risk in cancer
	morbidity for residents around municipal solid waste incinerators.
	The aim of this paper is to show how important are advanced tools
	and statistical techniques to better assess weak associations between
	the risk of cancer and past environmental exposures.\&\#10;Methods:\&\#10;The
	steps to evaluate the association between the risk of cancer and
	the exposure to incinerators, from the assessment of exposure to
	the definition of the confounding variables and the statistical analysis
	carried out are detailed and discussed. Dispersion modelling was
	used to assess exposure to sixteen incinerators. A geographical information
	system was developed to define an index of exposure at the IRIS level
	that is the geographical unit we considered. Population density,
	rural/urban status, socio-economic deprivation, exposure to air pollution
	from traffic and from other industries were considered as potential
	confounding factors and defined at the IRIS level. Generalized additive
	models and Bayesian hierarchical models were used to estimate the
	association between the risk of cancer and the index of exposure
	to incinerators accounting for the confounding factors.\&\#10;Results:\&\#10;Modelling
	to assess the exposure to municipal solid waste incinerators allowed
	accounting for factors known to influence the exposure (meteorological
	data, point source characteristics, topography). The statistical
	models defined allowed modelling extra-Poisson variability and also
	non-linear relationships between the risk of cancer and the exposure
	to incinerators and the confounders.\&\#10;Conclusions:\&\#10;In
	most epidemiological studies distance is still used as a proxy for
	exposure leading to significant exposure missclassification. Additionally,
	in geographical correlation studies the non-linear relationships
	are usually not accounted for in the statistical analysis. In studies
	of weak associations it is important to use advanced methods to better
	assess dose-response relationships with disease risk.}},
  citeulike-article-id = {4692686},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-8-31},
  day = {28},
  doi = {10.1186/1476-072X-8-31},
  issn = {1476-072X},
  keywords = {cancer, clustering, geography, health, modeling},
  posted-at = {2009-05-31 20:35:45},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-072X-8-31}
}

@ARTICLE{Grimson1979,
  author = {Grimson, Roger C.},
  title = {{The clustering of disease}},
  journal = {Mathematical Biosciences},
  year = {1979},
  volume = {46},
  pages = {257--278},
  number = {3-4},
  month = {October},
  abstract = {{Formulas which are useful in studying disease epidemicity are presented,
	and the combinatorial and asymptotic rationales of the underlying
	model are explored. The Ederer-Meyer-Mantel cluster test can be used
	to see if clustering of a disease exists in time and space; if clustering
	exists, then our cluster model may be used to advantage on the same
	data in order to characterize key epidemiologic features of the disease.
	Applying the model to type-A hepatitis, we describe a new epidemic
	pattern of this disease. We introduce a new mathematical rationale
	for epidemic processes, and we provide a convenient framework for
	describing disease clusters.}},
  citeulike-article-id = {8658276},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/0025-5564(79)90072-5},
  citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/0025556479900725},
  doi = {10.1016/0025-5564(79)90072-5},
  issn = {00255564},
  keywords = {cluster, cluster\_detection, disease, health},
  owner = {ijt1},
  posted-at = {2011-01-20 16:32:41},
  priority = {2},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1016/0025-5564(79)90072-5}
}

@ARTICLE{citeulike:3127334,
  author = {Guo, D.},
  title = {{Regionalization with dynamically constrained agglomerative clustering
	and partitioning (REDCAP)}},
  journal = {International Journal of Geographical Information Science},
  year = {2008},
  volume = {22},
  pages = {801--823},
  number = {7},
  citeulike-article-id = {3127334},
  citeulike-linkout-0 = {http://dx.doi.org/10.1080/13658810701674970},
  citeulike-linkout-1 = {http://www.ingentaconnect.com/content/tandf/tgis/2008/00000022/00000007/art00004},
  doi = {10.1080/13658810701674970},
  issn = {1365-8816},
  keywords = {clustering, geography, zones},
  posted-at = {2008-08-16 15:57:49},
  priority = {2},
  publisher = {Taylor and Francis Ltd},
  url = {http://dx.doi.org/10.1080/13658810701674970}
}

@ARTICLE{citeulike:1382154,
  author = {Guo, Diansheng and Chen, Jin and Maceachren, A. M. and Liao, Ke},
  title = {{A Visualization System for Space-Time and Multivariate Patterns
	(VIS-STAMP)}},
  journal = {Visualization and Computer Graphics, IEEE Transactions on},
  year = {2006},
  volume = {12},
  pages = {1461--1474},
  number = {6},
  abstract = {{The research reported here integrates computational, visual and cartographic
	methods to develop a geovisual analytic approach for exploring and
	understanding spatio-temporal and multivariate patterns. The developed
	methodology and tools can help analysts investigate complex patterns
	across multivariate, spatial and temporal dimensions via clustering,
	sorting and visualization. Specifically, the approach involves a
	self-organizing map, a parallel coordinate plot, several forms of
	reorderable matrices (including several ordering methods), a geographic
	small multiple display and a 2-dimensional cartographic color design
	method. The coupling among these methods leverages their independent
	strengths and facilitates a visual exploration of patterns that are
	difficult to discover otherwise. The visualization system we developed
	supports overview of complex patterns and through a variety of interactions,
	enables users to focus on specific patterns and examine detailed
	views. We demonstrate the system with an application to the IEEE
	InfoVis 2005 contest data set, which contains time-varying, geographically
	referenced and multivariate data for technology companies in the
	US}},
  citeulike-article-id = {1382154},
  citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1703367},
  keywords = {algorithm, clustering, data-mining, infovis, spatiotemporal, visualization},
  posted-at = {2007-06-12 21:25:01},
  priority = {3},
  url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1703367}
}

@ARTICLE{citeulike:841598,
  author = {Hanigan, Ivan and Hall, Gillian and Dear, Keith B. G.},
  title = {{A comparison of methods for calculating population exposure estimates
	of daily weather for health research}},
  journal = {International Journal of Health Geographics},
  year = {2006},
  volume = {5},
  pages = {38+},
  month = {September},
  abstract = {{To explain the possible effects of exposure to weather conditions
	on population health outcomes, weather data need to be calculated
	at a level in space and time that is appropriate for the health data.
	There are various ways of estimating exposure values from raw data
	collected at weather stations but the rationale for using one technique
	rather than another; the significance of the difference in the values
	obtained; and the effect these have on a research question are factors
	often not explicitly considered. In this study we compare different
	techniques for allocating weather data observations to small geographical
	areas and different options for weighting averages of these observations
	when calculating estimates of daily precipitation and temperature
	for Australian Postal Areas. Options that weight observations based
	on distance from population centroids and population size are more
	computationally intensive but give estimates that conceptually are
	more closely related to the experience of the population.}},
  citeulike-article-id = {841598},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-5-38},
  day = {13},
  doi = {10.1186/1476-072X-5-38},
  issn = {1476-072X},
  keywords = {analysis, clustering, disease, geospatial, health},
  posted-at = {2006-09-13 19:23:08},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-072X-5-38}
}

@ARTICLE{citeulike:577224,
  author = {Hearst, Marti A.},
  title = {{Clustering versus faceted categories for information exploration}},
  journal = {Commun. ACM},
  year = {2006},
  volume = {49},
  pages = {59--61},
  number = {4},
  month = {April},
  abstract = {{Note: OCR errors may be found in this Reference List extracted from
	the full text article. ACM has opted to expose the complete List
	rather than only correct and linked references.}},
  address = {New York, NY, USA},
  citeulike-article-id = {577224},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1121949.1121983},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1121949.1121983},
  doi = {10.1145/1121949.1121983},
  issn = {0001-0782},
  keywords = {clustering, exploration, facets, information, search},
  posted-at = {2008-06-13 18:35:41},
  priority = {0},
  publisher = {ACM},
  url = {http://dx.doi.org/10.1145/1121949.1121983}
}

@INPROCEEDINGS{citeulike:453993,
  author = {Hearst, Marti A. and Pedersen, Jan O.},
  title = {{Reexamining the cluster hypothesis: scatter/gather on retrieval
	results}},
  booktitle = {SIGIR '96: Proceedings of the 19th annual international ACM SIGIR
	conference on Research and development in information retrieval},
  year = {1996},
  pages = {76--84},
  address = {New York, NY, USA},
  publisher = {ACM Press},
  abstract = {{We present Scatter/Gather, a cluster-based document browsing method,
	as an alternative to ranked titles for the organization and viewing
	of retrieval results. We systematically evaluate Scatter/Gather in
	this context and nd signicant improvements over similarity search
	ranking alone. This result provides evidence validating the cluster
	hypothesis which states that relevant documents tend to be more similar
	to each other than to non-relevant documents. We describe a system
	employing Scatter/Gather and demonstrate that users are able to use
	this system close to its full potential.}},
  citeulike-article-id = {453993},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=243216},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/243199.243216},
  doi = {10.1145/243199.243216},
  isbn = {0897917928},
  keywords = {clustering, document, index, information-retrieval, knowledge-management},
  posted-at = {2007-12-03 20:08:15},
  priority = {0},
  url = {http://dx.doi.org/10.1145/243199.243216}
}

@ARTICLE{citeulike:665752,
  author = {Hinman, Sarah and Blackburn, Jason and Curtis, Andrew},
  title = {{Spatial and temporal structure of typhoid outbreaks in Washington,
	D.C., 1906-1909: evaluating local clustering with the Gi* statistic}},
  journal = {International Journal of Health Geographics},
  year = {2006},
  volume = {5},
  number = {1},
  abstract = {{BACKGROUND:To better understand the distribution of typhoid outbreaks
	in Washington, D.C., the U.S. Public Health Service (PHS) conducted
	four investigations of typhoid fever. These studies included maps
	of cases reported between 1 May - 31 October 1906 - 1909. These data
	were entered into a GIS database and analyzed using Ripley's K-function
	followed by the Gi* statistic in yearly intervals to evaluate spatial
	clustering, the scale of clustering, and the temporal stability of
	these clusters.RESULTS:The Ripley's K-function indicated no global
	spatial autocorrelation. The Gi* statistic indicated clustering of
	typhoid at multiple scales across the four year time period, refuting
	the conclusions drawn in all four PHS reports concerning the distribution
	of cases. While the PHS reports suggested an even distribution of
	the disease, this study quantified both areas of localized disease
	clustering, as well as mobile larger regions of clustering. Thus,
	indicating both highly localized and periodic generalized sources
	of infection within the city.CONCLUSION:The methodology applied in
	this study was useful for evaluating the spatial distribution and
	annual-level temporal patterns of typhoid outbreaks in Washington,
	D.C. from 1906 to 1909. While advanced spatial analyses of historical
	data sets must be interpreted with caution, this study does suggest
	that there is utility in these types of analyses and that they provide
	new insights into the urban patterns of typhoid outbreaks during
	the early part of the twentieth century.}},
  citeulike-article-id = {665752},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-5-13},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/16566830},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=16566830},
  doi = {10.1186/1476-072X-5-13},
  keywords = {clustering, historic, spatiotemporal, typhoid},
  posted-at = {2006-05-22 20:23:12},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-072X-5-13}
}

@ARTICLE{citeulike:267327,
  author = {Hinneburg, A. and Keim, D. A. and Wawryniuk, M.},
  title = {{HD-Eye: visual mining of high-dimensional data}},
  journal = {Computer Graphics and Applications, IEEE},
  year = {1999},
  volume = {19},
  pages = {22--31},
  number = {5},
  abstract = {{Clustering in high-dimensional databases poses an important problem.
	However, we can apply a number of different clustering algorithms
	to high-dimensional data. The authors consider how an advanced clustering
	algorithm combined with new visualization methods interactively clusters
	data more effectively. Experiments show these techniques improve
	the data mining process}},
  citeulike-article-id = {267327},
  citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=788795},
  keywords = {clustering, data-mining, information, infovis, visualization},
  posted-at = {2006-04-22 21:34:09},
  priority = {2},
  url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=788795}
}

@ARTICLE{citeulike:83751,
  author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.},
  title = {{Data clustering: a review}},
  journal = {ACM Comput. Surv.},
  year = {1999},
  volume = {31},
  pages = {264--323},
  number = {3},
  month = {September},
  abstract = {{Clustering is the unsupervised classification of patterns (observations,
	data items, or feature vectors) into groups (clusters). The clustering
	problem has been addressed in many contexts and by researchers in
	many disciplines; this reflects its broad appeal and usefulness as
	one of the steps in exploratory data analysis. However, clustering
	is a difficult problem combinatorially, and differences in assumptions
	and contexts in different communities has made the transfer of useful
	generic concepts and methodologies slow to occur. This paper presents
	an overview of pattern clustering methods from a statistical pattern
	recognition perspective, with a goal of providing useful advice and
	references to fundamental concepts accessible to the broad community
	of clustering practitioners. We present a taxonomy of clustering
	techniques, and identify cross-cutting themes and recent advances.
	We also describe some important applications of clustering algorithms
	such as image segmentation, object recognition, and information retrieval.}},
  address = {New York, NY, USA},
  citeulike-article-id = {83751},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=331504},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/331499.331504},
  doi = {10.1145/331499.331504},
  issn = {0360-0300},
  keywords = {algorithm, analysis, classification, cluster, clustering},
  posted-at = {2007-03-26 18:29:19},
  priority = {3},
  publisher = {ACM},
  url = {http://dx.doi.org/10.1145/331499.331504}
}

@INPROCEEDINGS{citeulike:6600137,
  author = {Joshi, Deepti and Samal, Ashok and Soh, Leen K.},
  title = {{A dissimilarity function for clustering geospatial polygons}},
  booktitle = {GIS '09: Proceedings of the 17th ACM SIGSPATIAL International Conference
	on Advances in Geographic Information Systems},
  year = {2009},
  pages = {384--387},
  address = {New York, NY, USA},
  publisher = {ACM},
  abstract = {{The traditional point-based clustering algorithms when applied to
	geospatial polygons may produce clusters that are spatially disjoint
	due to their inability to consider various types of spatial relationships
	between polygons. In this paper, we propose to represent geospatial
	polygons as sets of spatial and non-spatial attributes. By representing
	a polygon as a set of spatial and non-spatial attributes we are able
	to take into account all the properties of a polygon (such as structural,
	topological and directional) that were ignored while using point-based
	representation of polygons, and that aid in the formation of high
	quality clusters. Based on this framework we propose a dissimilarity
	function that can be plugged into common state-of-the-art spatial
	clustering algorithms. The result is clusters of polygons that are
	more compact in terms of cluster validity and spatial contiguity.
	We show the effectiveness and robustness of our approach by applying
	our dissimilarity function on the traditional k -means clustering
	algorithm and testing it on a watershed dataset.}},
  citeulike-article-id = {6600137},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1653771.1653825},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1653771.1653825},
  doi = {10.1145/1653771.1653825},
  isbn = {978-1-60558-649-6},
  keywords = {clustering, polygons},
  location = {Seattle, Washington},
  posted-at = {2010-01-28 16:41:59},
  priority = {2},
  url = {http://dx.doi.org/10.1145/1653771.1653825}
}

@ARTICLE{Knox1994,
  author = {Knox, E. G.},
  title = {{Leukaemia clusters in childhood: geographical analysis in Britain.}},
  journal = {Journal of Epidemiology and Community Health},
  year = {1994},
  volume = {48},
  pages = {369--376},
  number = {4},
  month = {August},
  abstract = {{STUDY OBJECTIVE--To validate previously demonstrated spatial clustering
	of childhood leukaemias by showing relative proximities of selected
	map features to cluster locations, compared with control locations.
	If clusters are real, then they are likely to be close to a determining
	hazard. DESIGN--Cluster postcode loci and partially matched control
	postcodes were compared in terms of distances to railways, main roads,
	churches, surface water, woodland areas, and railside industrial
	installations. Further supporting comparisons between non-clustered
	cases and random postcode controls with those map features representable
	as single grid points were made. SETTING--England, Wales, and Scotland
	1966-83. SUBJECTS--Grid referenced registrations of 9406 childhood
	leukaemias and non-Hodgkin's lymphomas, including 264 pairs (or more)
	separated by < 150 m, and grid references of random postcodes in
	equal numbers. MAIN RESULTS--The 264 clusters showed relative proximities
	(or the inverse) to several map features, of which the most powerful
	was an association with railways. The non-railway associations seemed
	to be statistically indirect. Some railside industrial installations,
	identified from a railway atlas, also showed relative proximities
	to leukaemia clusters, as well as to non-clustered cases, but did
	not "explain" the railway effect. These installations, with seemingly
	independent geographical associations, included oil refineries, petrochemical
	plants, oil storage and oil distribution depots, power stations,
	and steelworks. CONCLUSIONS--The previously shown childhood leukaemia
	clusters are confirmed to be non-random through their systematic
	associations with certain map features when compared with the control
	locations. The common patterns of close association of clustered
	and non-clustered cases imply a common aetiological component arising
	from a common environmental hazard--namely the use of fossil fuels,
	especially petroleum.}},
  citeulike-article-id = {3391803},
  citeulike-linkout-0 = {http://dx.doi.org/10.1136/jech.48.4.369},
  citeulike-linkout-1 = {http://jech.bmj.com/content/48/4/369.abstract},
  citeulike-linkout-2 = {http://jech.bmj.com/content/48/4/369.full.pdf},
  citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/7964336},
  citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=7964336},
  day = {1},
  doi = {10.1136/jech.48.4.369},
  issn = {0143-005X},
  keywords = {cancer, cluster, cluster\_detection, health},
  owner = {ijt1},
  posted-at = {2011-01-20 16:37:04},
  priority = {2},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1136/jech.48.4.369}
}

@ARTICLE{Kulldorff1997,
  author = {Kulldorff, M.},
  title = {{A spatial scan statistic}},
  journal = {Communications in Statistics-Theory and Methods},
  year = {1997},
  volume = {26},
  pages = {1481--1496},
  number = {6},
  citeulike-article-id = {8657275},
  citeulike-linkout-0 = {http://www.satscan.org/papers/k-cstm1997.pdf},
  keywords = {cluster, cluster\_detection, health, space},
  posted-at = {2011-01-20 15:50:52},
  priority = {3},
  url = {http://www.satscan.org/papers/k-cstm1997.pdf}
}

@ARTICLE{citeulike:1463380,
  author = {Kulldorff, Martin and Tango, Toshiro and Park, Peter J.},
  title = {{Power comparisons for disease clustering tests}},
  journal = {Computational Statistics \& Data Analysis},
  year = {2003},
  volume = {42},
  pages = {665--684},
  number = {4},
  month = {April},
  abstract = {{Many different methods have been proposed to test for geographical
	disease clustering, and more generally, for spatial clustering of
	any type of observations while adjusting for an inhomogeneous background
	population generating the observations. Despite the many proposed
	test statistics, there has been few formal comparisons conducted.
	We present a collection of 1,220,000 simulated benchmark data sets
	generated under 51 different cluster models and the null hypothesis,
	to be used for power evaluations. We then use these data sets to
	compare the power of the spatial scan statistic, the maximized excess
	events test and the nonparametric M statistic. All have good power,
	the first having an advantage for localized hot-spot type clusters
	and the second for global clustering where randomly located cases
	generate other cases close by. By making the simulated data sets
	publicly available, new tests can easily be compared with previously
	evaluated tests by analyzing the same benchmark data.}},
  citeulike-article-id = {1463380},
  citeulike-linkout-0 = {http://www.sciencedirect.com/science/article/B6V8V-472JRC1-J/2/4d79cd8f5e9b796e9590372756fcd3eb},
  day = {28},
  keywords = {algorithm, analysis, calibration, clustering, evaluation, testing},
  posted-at = {2007-07-17 18:41:58},
  priority = {5},
  url = {http://www.sciencedirect.com/science/article/B6V8V-472JRC1-J/2/4d79cd8f5e9b796e9590372756fcd3eb}
}

@ELECTRONIC{citeulike:484851,
  author = {Lambiotte, R. and Ausloos, M.},
  month = {Dec},
  year = {2005},
  title = {{Collaborative tagging as a tripartite network}},
  url = {http://arxiv.org/abs/cs.DS/0512090},
  abstract = {{We describe online collaborative communities by tripartite networks,
	the nodes being persons, items and tags. We introduce projection
	methods in order to uncover the structures of the networks, i.e.
	communities of users, genre families... <br />To do so, we focus
	on the correlations between the nodes, depending on their profiles,
	and use percolation techniques that consist in removing less correlated
	links and observing the shaping of disconnected islands. The structuring
	of the network is visualised by using a tree representation. The
	notion of diversity in the system is also discussed.}},
  archiveprefix = {arXiv},
  citeulike-article-id = {484851},
  citeulike-linkout-0 = {http://arxiv.org/abs/cs.DS/0512090},
  citeulike-linkout-1 = {http://arxiv.org/pdf/cs.DS/0512090},
  day = {29},
  eprint = {cs.DS/0512090},
  keywords = {clustering, collaborative, folksonomy, tagging, taxonomy},
  posted-at = {2006-05-11 22:05:42},
  priority = {3}
}

@ARTICLE{Lawson2006,
  author = {Lawson, Andrew B.},
  title = {{Disease cluster detection: a critique and a Bayesian proposal}},
  journal = {Statist. Med.},
  year = {2006},
  volume = {25},
  pages = {897--916},
  number = {5},
  month = {March},
  abstract = {{This paper reviews issues in the analysis of non-focussed clustering,
	and proposes a novel approach to cluster modelling that can be used
	in a surveillance context. The novel approach involves the use of
	local likelihood models for the analysis of clustering in small area
	health data. Local likelihood is used when interdependence between
	data events at locations is modelled directly, as opposed to the
	modelling of a hidden process of cluster centres. This approach allows
	the use of conventional posterior sampling. It also allows a less
	parameterized approach to the form of clusters detected. The idea
	of a spatially dependent lasso which provides the local maxima for
	the aggregation of locations is considered as an approximation. The
	methods are applied to a well known data set and compared with Satscan,
	and a conditional logistic Bayesian model. Copyright {\copyright}
	2006 John Wiley \& Sons, Ltd.}},
  address = {Arnold School of Public Health, University of South Carolina, USA.
	alawson@gwm.sc.edu},
  citeulike-article-id = {2635997},
  citeulike-linkout-0 = {http://dx.doi.org/10.1002/sim.2417},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/16453377},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=16453377},
  day = {15},
  doi = {10.1002/sim.2417},
  issn = {0277-6715},
  keywords = {bayesian, cluster, cluster\_detection, disease, health},
  owner = {ijt1},
  posted-at = {2011-01-20 16:49:34},
  priority = {2},
  publisher = {John Wiley \& Sons, Ltd.},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1002/sim.2417}
}

@MISC{citeulike:1368923,
  author = {Le Grand, B\&eacute;n\&eacute;dicte and Soto, Michel},
  title = {{Visualisation of the Semantic Web: Topic Maps Visualisation}},
  abstract = {{knowledge representation and information management by building a
	structured semantic network above information resources. Our research
	at LIP6 aims at visualizing this semantic layer efficiently, which
	is a critical issue as Topic Maps may contain millions of elements.}},
  citeulike-article-id = {1368923},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.873},
  keywords = {classification, clustering, disambiguation, entities, infovis, openstandards,
	semanticweb, visualization},
  posted-at = {2007-06-07 18:27:47},
  priority = {4},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.873}
}

@ARTICLE{citeulike:8156764,
  author = {Leibovici, D. G. and Bastin, L. and Jackson, M.},
  title = {{Higher-order Co-occurrences for exploratory point pattern analysis
	and decision tree clustering on spatial data}},
  journal = {Computers \& Geosciences},
  year = {2010},
  month = {October},
  abstract = {{Analyzing geographical patterns by collocating events, objects or
	their attributes has a long history in surveillance and monitoring,
	and is particularly applied in environmental contexts, such as ecology
	or epidemiology. The identification of patterns or structures at
	some scales can be addressed using spatial statistics, particularly
	marked point processes methodologies. Classification and regression
	trees are also related to this goal of finding â€� patternsâ€� by
	deducing the hierarchy of influence of variables on a dependent outcome.
	Such variable selection methods have been applied to spatial data,
	but, often without explicitly acknowledging the spatial dependence.
	Many methods routinely used in exploratory point pattern analysis
	are second-order statistics, used in a univariate context, though
	there is also a wide literature on modelling methods for multivariate
	point pattern processes. This paper proposes an exploratory approach
	for multivariate spatial data using higher-order statistics built
	from co-occurrences of events or marks given by the point processes.
	A spatial entropy measure, derived from these multinomial distributions
	of co-occurrences at a given order, constitutes the basis of the
	proposed exploratory methods.}},
  citeulike-article-id = {8156764},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.cageo.2010.06.006},
  citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0098300410002736},
  day = {29},
  doi = {10.1016/j.cageo.2010.06.006},
  issn = {00983004},
  keywords = {clustering, exploratory, spatial, spatial\_analysis},
  posted-at = {2010-11-02 00:31:23},
  priority = {2},
  url = {http://dx.doi.org/10.1016/j.cageo.2010.06.006}
}

@ARTICLE{citeulike:1684641,
  author = {Lian, Min and Warner, Ronald D. and Alexander, James L. and Dixon,
	Kenneth R.},
  title = {{Using geographic information systems and spatial and space-time
	scan statistics for a population-based risk analysis of the 2002
	equine West Nile epidemic in six contiguous regions of Texas}},
  journal = {International Journal of Health Geographics},
  year = {2007},
  volume = {6},
  pages = {42+},
  month = {September},
  citeulike-article-id = {1684641},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-6-42},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17888159},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17888159},
  day = {21},
  doi = {10.1186/1476-072X-6-42},
  issn = {1476-072X},
  keywords = {clustering, epidemiology, gis, health, space-time, spatial, westnilevirus},
  posted-at = {2007-10-09 17:52:39},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-072X-6-42}
}

@ARTICLE{citeulike:1392758,
  author = {Lin and Ge and Zhang and Tonglin},
  title = {{Loglinear Residual Tests of Moran's I Autocorrelation and their
	Applications to Kentucky Breast Cancer Data}},
  journal = {Geographical Analysis},
  year = {2007},
  volume = {39},
  pages = {293--310},
  number = {3},
  month = {July},
  citeulike-article-id = {1392758},
  citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1538-4632.2007.00705.x},
  citeulike-linkout-1 = {http://www.ingentaconnect.com/content/bsc/gean/2007/00000039/00000003/art00003},
  doi = {10.1111/j.1538-4632.2007.00705.x},
  issn = {0016-7363},
  keywords = {algorithm, analysis, cancer, cluster, clustering, epidemiology, geography,
	health, multivariate},
  posted-at = {2007-06-19 20:42:24},
  priority = {2},
  publisher = {Blackwell Publishing},
  url = {http://dx.doi.org/10.1111/j.1538-4632.2007.00705.x}
}

@ARTICLE{citeulike:1719303,
  author = {Lin, Yongjing and Li, Wenyuan and Chen, Keke and Liu, Ying},
  title = {{A document clustering and ranking system for exploring MEDLINE citations.}},
  journal = {Journal of the American Medical Informatics Association : JAMIA},
  year = {2007},
  volume = {14},
  pages = {651--661},
  number = {5},
  month = {September},
  abstract = {{OBJECTIVE: A major problem faced in biomedical informatics involves
	how best to present information retrieval results. When a single
	query retrieves many results, simply showing them as a long list
	often provides poor overview. With a goal of presenting users with
	reduced sets of relevant citations, this study developed an approach
	that retrieved and organized MEDLINE citations into different topical
	groups and prioritized important citations in each group. DESIGN:
	A text mining system framework for automatic document clustering
	and ranking organized MEDLINE citations following simple PubMed queries.
	The system grouped the retrieved citations, ranked the citations
	in each cluster, and generated a set of keywords and MeSH terms to
	describe the common theme of each cluster. MEASUREMENTS: Several
	possible ranking functions were compared, including citation count
	per year (CCPY), citation count (CC), and journal impact factor (JIF).
	We evaluated this framework by identifying as "important" those articles
	selected by the Surgical Oncology Society. RESULTS: Our results showed
	that CCPY outperforms CC and JIF, i.e., CCPY better ranked important
	articles than did the others. Furthermore, our text clustering and
	knowledge extraction strategy grouped the retrieval results into
	informative clusters as revealed by the keywords and MeSH terms extracted
	from the documents in each cluster. CONCLUSIONS: The text mining
	system studied effectively integrated text clustering, text summarization,
	and text ranking and organized MEDLINE retrieval results into different
	topical groups.}},
  citeulike-article-id = {1719303},
  citeulike-linkout-0 = {http://dx.doi.org/10.1197/jamia.M2215},
  citeulike-linkout-1 = {http://www.jamia.org/cgi/content/abstract/14/5/651},
  citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17600104},
  citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17600104},
  day = {1},
  doi = {10.1197/jamia.M2215},
  issn = {1067-5027},
  keywords = {citations, clustering, document, exploration, ranking},
  posted-at = {2008-03-25 16:21:53},
  priority = {2},
  url = {http://dx.doi.org/10.1197/jamia.M2215}
}

@ARTICLE{citeulike:5781729,
  author = {MacEachren, A. M. and Brewer, C. A. and Pickle, L. W.},
  title = {{Visualizing georeferenced data: representing reliability of health
	statistics}},
  journal = {Environment and Planning A},
  year = {1998},
  volume = {30},
  pages = {1547--1561},
  number = {9},
  abstract = {{The power of human vision to synthesize information and recognize
	pattern is fundamental to the success of visualization as a scientific
	method. This same power can mislead investigators who use visualization
	to explore georeferenced data -- if data reliability is not addressed
	directly in the visualization process. Here, we apply an integrated
	cognitive-semiotic approach to devise and test three methods for
	depicting reliability of georeferenced health data. The first method
	makes use of adjacent maps, one for data and one for reliability.
	This form of paired representation is compared to two methods in
	which data and reliability are spatially coincident (on a single
	map). A novel method for coincident visually separable depiction
	of data and data reliability on mortality maps (using a color fill
	to represent data and a texture overlay to represent reliability)
	is found to be effective in allowing map users to recognize unreliable
	data without interfering with their ability to notice clusters and
	characterize patterns in mortality rates. A coincident visually integral
	depiction (using color characteristics to represent both data and
	reliability) is found to inhibit perception of clusters that contain
	some enumeration units with unreliable data, and to make it difficult
	for users to consider data and reliability independently.}},
  citeulike-article-id = {5781729},
  citeulike-linkout-0 = {http://dx.doi.org/10.1068/a301547},
  citeulike-linkout-1 = {http://www.envplan.com/abstract.cgi?id=a301547},
  doi = {10.1068/a301547},
  keywords = {clustering, geovisualization, health, statistics, visualization},
  posted-at = {2009-09-14 20:34:48},
  priority = {3},
  publisher = {Pion Ltd},
  url = {http://dx.doi.org/10.1068/a301547}
}

@INPROCEEDINGS{citeulike:595495,
  author = {Macgill, James and Openshaw, Stan and Turton, Ian},
  title = {{Web-based multi-agent spatial analysis tools}},
  booktitle = {GeoComputation},
  year = {1999},
  citeulike-article-id = {595495},
  citeulike-linkout-0 = {http://www.geovista.psu.edu/sites/geocomp99/Gc99/069/gc\_069.htm},
  keywords = {analysis, boids, clustering, flock, spatial, tool, web-based},
  posted-at = {2006-04-22 20:56:43},
  priority = {0},
  url = {http://www.geovista.psu.edu/sites/geocomp99/Gc99/069/gc\_069.htm}
}

@ARTICLE{citeulike:6557493,
  author = {Maciejewski, Ross and Rudolph, Stephen and Hafen, Ryan and Abusalah,
	Ahmad M. and Yakout, Mohamed and Ouzzani, Mourad and Cleveland, William
	S. and Grannis, Shaun J. and Ebert, David S.},
  title = {{A Visual Analytics Approach to Understanding Spatiotemporal Hotspots}},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {5555},
  volume = {99},
  pages = {205--220},
  number = {2},
  month = {August},
  abstract = {{As data sources become larger and more complex, the ability to effectively
	explore and analyze patterns among varying sources becomes a critical
	bottleneck in analytic reasoning. Incoming data contain multiple
	variables, high signal-to-noise ratio, and a degree of uncertainty,
	all of which hinder exploration, hypothesis generation/exploration,
	and decision making. To facilitate the exploration of such data,
	advanced tool sets are needed that allow the user to interact with
	their data in a visual environment that provides direct analytic
	capability for finding data aberrations or hotspots. In this paper,
	we present a suite of tools designed to facilitate the exploration
	of spatiotemporal data sets. Our system allows users to search for
	hotspots in both space and time, combining linked views and interactive
	filtering to provide users with contextual information about their
	data and allow the user to develop and explore their hypotheses.
	Statistical data models and alert detection algorithms are provided
	to help draw user attention to critical areas. Demographic filtering
	can then be further applied as hypotheses generated become fine tuned.
	This paper demonstrates the use of such tools on multiple geospatiotemporal
	data sets.}},
  address = {Los Alamitos, CA, USA},
  citeulike-article-id = {6557493},
  citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2009.100},
  citeulike-linkout-1 = {http://dx.doi.org/10.1109/TVCG.2009.100},
  citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=5226628},
  day = {28},
  doi = {10.1109/TVCG.2009.100},
  issn = {1077-2626},
  keywords = {clustering, infovis, spatiotemporal, visualization},
  posted-at = {2010-01-18 18:35:55},
  priority = {2},
  publisher = {IEEE Computer Society},
  url = {http://dx.doi.org/10.1109/TVCG.2009.100}
}

@ARTICLE{citeulike:2316348,
  author = {Mackinnon, Jill A. and Duncan, Robert C. and Huang, Youjie and Lee,
	David J. and Fleming, Lora E. and Voti, Lydia and Rudolph, Mark and
	Wilkinson, James D.},
  title = {{Detecting an Association between Socioeconomic Status and Late Stage
	Breast Cancer Using Spatial Analysis and Area-Based Measures}},
  journal = {Cancer Epidemiol Biomarkers Prev},
  year = {2007},
  volume = {16},
  pages = {756--762},
  number = {4},
  month = {April},
  abstract = {{Objectives: To assess the relationship between socioeconomic status
	(SES) and late stage breast cancer using the cluster detection software
	SaTScan and U.S. census-derived area-based socioeconomic measures.
	Materials and Methods: Florida's 18,683 women diagnosed with late
	stage breast cancer (regional or distant stage) between 1998 and
	2002 as identified by Florida's population-based, statewide, incidence
	registry were analyzed by SaTScan to identify areas of higher-than-expected
	incidence. The relationship between SES and late stage breast cancer
	was assessed at the neighborhood (block group) level by combining
	the SaTScan results with area-based SES data. Results: SaTScan identified
	767 of Florida's 9,112 block groups that had higher-than-expected
	incidence of late stage breast cancer. After controlling for patient
	level insurance status, county level mammography prevalence, and
	urban/rural residence in the logistic regression model, women living
	in neighborhoods of severe and near poverty were respectively 3.0
	and 1.6 times more likely to live in areas of higher-than-expected
	incidence of late stage breast cancer when compared with women living
	in nonpoverty. Additionally, areas in the lowest quartile of mammography
	usage were almost seven times more likely to have higher-than-expected
	incidence than areas in the higher quartiles. Conclusions: In addition
	to confirming the importance of mammography, results from the present
	study suggest that "where" you live plays an important role in defining
	the risk of presenting with late stage breast cancer. Additional
	research is urgently needed to understand this risk and to leverage
	the strengths and resources present in all communities to lower the
	late stage breast cancer burden. (Cancer Epidemiol Biomarkers Prev
	2007;16(4):756-62) 10.1158/1055-9965.EPI-06-0392}},
  citeulike-article-id = {2316348},
  citeulike-linkout-0 = {http://dx.doi.org/10.1158/1055-9965.EPI-06-0392},
  citeulike-linkout-1 = {http://cebp.aacrjournals.org/cgi/content/abstract/16/4/756},
  citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17416767},
  citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17416767},
  day = {1},
  doi = {10.1158/1055-9965.EPI-06-0392},
  keywords = {cancer, clustering, epidemiology, geographic, health, socioeconomic,
	spatial},
  posted-at = {2008-01-31 20:43:17},
  priority = {2},
  url = {http://dx.doi.org/10.1158/1055-9965.EPI-06-0392}
}

@ARTICLE{Mantel1967,
  author = {Mantel, Nathan},
  title = {{The Detection of Disease Clustering and a Generalized Regression
	Approach}},
  journal = {Cancer Research},
  year = {1967},
  volume = {27},
  pages = {209--220},
  number = {2 Part 1},
  month = {February},
  abstract = {{The problem of identifying subtle time-space clustering of disease,
	as may be occurring in leukemia, is described and reviewed. Published
	approaches, generally associated with studies of leukemia, not dependent
	on knowledge of the underlying population for their validity, are
	directed towards identifying clustering by establishing a relationship
	between the temporal and the spatial separations for the n(n - 1)/2
	possible pairs which can be formed from the n observed cases of disease.
	Here it is proposed that statistical power can be improved by applying
	a reciprocal transform to these separations. While a permutational
	approach can give valid probability levels for any observed association,
	for reasons of practicability, it is suggested that the observed
	association be tested relative to its permutational variance. Formulas
	and computational procedures for doing so are given.While the distance
	measures between points represent symmetric relationships subject
	to mathematical and geometric regularities, the variance formula
	developed is appropriate for arbitrary relationships. Simplified
	procedures are given for the case of symmetric and skew-symmetric
	relationships. The general procedure is indicated as being potentially
	useful in other situations as, for example, the study of interpersonal
	relationships. Viewing the procedure as a regression approach, the
	possibility for extending it to nonlinear and multivariate situations
	is suggested.Other aspects of the problem and of the procedure developed
	are discussed.Similarly, pure temporal clustering can be identified
	by a study of incidence rates in periods of widespread epidemics.
	In point of fact, many epidemics of communicable diseases are somewhat
	local in nature and so these do actually constitute temporal-spatial
	clusters. For leukemia and similar diseases in which cases seem to
	arise substantially at random rather than as clear-cut epidemics,
	it is necessary to devise sensitive and efficient procedures for
	detecting any nonrandom component of disease occurrence.Various ingenious
	procedures which statisticians have developed for the detection of
	disease clustering are reviewed here. These procedures can be generalized
	so as to increase their statistical validity and efficiency. The
	technic to be given below for imparting statistical validity to the
	procedures already in vogue can be viewed as a generalized form of
	regression with possible useful application to problems arising in
	quite different contexts.}},
  citeulike-article-id = {3024473},
  citeulike-linkout-0 = {http://cancerres.aacrjournals.org/content/27/2\_Part\_1/209.abstract},
  citeulike-linkout-1 = {http://cancerres.aacrjournals.org/content/27/2\_Part\_1/209.full.pdf},
  citeulike-linkout-2 = {http://cancerres.aacrjournals.org/cgi/content/abstract/27/2\_Part\_1/209},
  citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/6018555},
  citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=6018555},
  day = {1},
  issn = {0008-5472},
  keywords = {cluster, cluster\_detection, disease, health, space-time},
  owner = {ijt1},
  posted-at = {2011-01-20 16:44:06},
  priority = {2},
  timestamp = {2011.01.20},
  url = {http://cancerres.aacrjournals.org/content/27/2\_Part\_1/209.abstract}
}

@ARTICLE{citeulike:2349299,
  author = {Marshall, Roger J.},
  title = {{A Review of Methods for the Statistical Analysis of Spatial Patterns
	of Disease}},
  abstract = {{A review of methods for the analysis of the geographical distribution
	of disease is presented. The topic is of increasing interest to statisticians,
	though much groundwork has been done by epidemiologists and medical
	geographers. Methods for the detection and testing for apparent clusters
	of disease, including those near a possible environmental hazard,
	are reviewed. Estimating regional mortality rates, possibly to construct
	disease maps, and methods for investigating the association between
	disease rates and social, demographic and environmental factors are
	discussed. In addition, statistical analysis of the spatial spread
	of epidemics is mentioned.}},
  citeulike-article-id = {2349299},
  citeulike-linkout-0 = {http://www.jstor.org/stable/2983152},
  keywords = {clustering, epidemiology, health},
  posted-at = {2008-02-07 14:43:10},
  priority = {2},
  url = {http://www.jstor.org/stable/2983152}
}

@INPROCEEDINGS{Naaman2004Automatic,
  author = {Naaman, M. and Song, Y. J. and Paepcke, A. and Garcia-Molina, H.},
  title = {{Automatic organization for digital photographs with geographic coordinates}},
  year = {2004},
  pages = {53--62},
  abstract = {{We describe PhotoCompas, a system that utilizes the time and location
	information embedded in digital photographs to automatically organize
	a personal photo collection. PhotoCompas produces browseable location
	and event hierarchies for the collection. These hierarchies are created
	using algorithms that interleave time and location to produce an
	organization that mimics the way people think about their photo collections.
	In addition, the algorithm annotates the generated hierarchy with
	geographical names. We tested our approach in case studies of three
	real-world collections and verified that the results are meaningful
	and useful for the collection owners.}},
  citeulike-article-id = {1449365},
  citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1336098},
  journal = {Digital Libraries, 2004. Proceedings of the 2004 Joint ACM/IEEE Conference
	on},
  keywords = {automatic, clustering, geocoding, photos, space-time},
  posted-at = {2007-10-26 14:48:47},
  priority = {4},
  url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1336098}
}

@ARTICLE{citeulike:4341093,
  author = {Neill, Daniel B.},
  title = {{An empirical comparison of spatial scan statistics for outbreak
	detection}},
  journal = {International Journal of Health Geographics},
  year = {2009},
  volume = {8},
  pages = {20+},
  month = {April},
  abstract = {{Background:\&\#10;The spatial scan statistic is a widely used statistical
	method for the automatic detection of disease clusters from syndromic
	data. Recent work in the disease surveillance community has proposed
	many variants of Kulldorff's original spatial scan statistic, including
	expectation-based Poisson and Gaussian statistics, and incorporates
	a variety of time series analysis methods to obtain baseline counts.
	We evaluate the detection performance of twelve variants of spatial
	scan, using synthetic outbreaks injected into four real-world public
	health datasets.\&\#10;Results:\&\#10;The relative performance of
	methods varies substantially depending on the size of the injected
	outbreak, the average daily count of the background data, and whether
	seasonal and day-of-week trends are present. The expectation-based
	Poisson (EBP) method achieves high performance across a wide range
	of datasets and outbreak sizes, making it useful in typical detection
	scenarios where the outbreak characteristics are not known. Kulldorff's
	statistic outperforms EBP for small outbreaks in datasets with high
	average daily counts, but has extremely poor detection power for
	outbreaks affecting more than 2/3 of the monitored locations. Randomization
	testing did not improve detection power for the four datasets considered,
	is computationally expensive, and can lead to high false positive
	rates.\&\#10;Conclusions:\&\#10;Our results suggest four main conclusions.
	First, spatial scan methods should be evaluated for a variety of
	different datasets and outbreak characteristics, since focusing only
	on a single scenario may give a misleading picture of which methods
	perform best. Second, we recommend the use of the expectation-based
	Poisson statistic rather than the traditional Kulldorff statistic
	when large outbreaks are of potential interest, or when average daily
	counts are low. Third, adjusting for seasonal and day-of-week trends
	can significantly improve performance in datasets where these trends
	are present. Finally, we recommend discontinuing the use of randomization
	testing in the spatial scan framework when sufficient historical
	data is available for empirical calibration of likelihood ratio scores.}},
  citeulike-article-id = {4341093},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-8-20},
  day = {16},
  doi = {10.1186/1476-072X-8-20},
  issn = {1476-072X},
  keywords = {cluster, clustering, disease, health, scan},
  posted-at = {2009-04-21 01:50:30},
  priority = {4},
  url = {http://dx.doi.org/10.1186/1476-072X-8-20}
}

@INPROCEEDINGS{Neill2004,
  author = {Neill, Daniel B. and Moore, Andrew W.},
  title = {{Rapid detection of significant spatial clusters}},
  booktitle = {KDD '04: Proceedings of the tenth ACM SIGKDD international conference
	on Knowledge discovery and data mining},
  year = {2004},
  pages = {256--265},
  address = {New York, NY, USA},
  publisher = {ACM Press},
  citeulike-article-id = {1822579},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1014052.1014082},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1014052.1014082},
  doi = {10.1145/1014052.1014082},
  isbn = {1581138881},
  keywords = {cluster, health, spatial},
  owner = {ijt1},
  posted-at = {2007-10-25 23:56:42},
  priority = {2},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1145/1014052.1014082}
}

@ARTICLE{citeulike:1450870,
  author = {Nunes, Carla},
  title = {{Tuberculosis incidence in Portugal: spatiotemporal clustering}},
  journal = {International Journal of Health Geographics},
  year = {2007},
  volume = {6},
  pages = {30+},
  month = {July},
  citeulike-article-id = {1450870},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-6-30},
  day = {11},
  doi = {10.1186/1476-072X-6-30},
  issn = {1476-072X},
  keywords = {analysis, cluster, clustering, disease, geospatial, space-time, spatiotemporal,
	tb, time},
  posted-at = {2007-07-12 19:49:20},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-072X-6-30}
}

@ARTICLE{citeulike:1110998,
  author = {Olsen, Kai A. and Korfhage, Robert R. and Sochats, Kenneth M. and
	Spring, Michael B. and Williams, James G.},
  title = {{Visualization of a document collection: the vibe system}},
  journal = {Inf. Process. Manage.},
  year = {1993},
  volume = {29},
  pages = {69--81},
  number = {1},
  address = {Tarrytown, NY, USA},
  citeulike-article-id = {1110998},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=153415.153421},
  citeulike-linkout-1 = {http://dx.doi.org/10.1016/0306-4573(93)90024-8},
  doi = {10.1016/0306-4573(93)90024-8},
  issn = {0306-4573},
  keywords = {analysis, clustering, information-retrieval, library, search, visualization,
	web},
  posted-at = {2007-02-19 21:38:38},
  priority = {3},
  publisher = {Pergamon Press, Inc.},
  url = {http://dx.doi.org/10.1016/0306-4573(93)90024-8}
}

@ARTICLE{Olsen1996,
  author = {Olsen, Sj\'{u}rdur F. and Martuzzi, Marco and Elliott, Paul},
  title = {{Cluster Analysis And Disease Mapping: Why, When, And How? A Step
	By Step Guide}},
  journal = {BMJ: British Medical Journal},
  year = {1996},
  volume = {313},
  number = {7061},
  abstract = {{Growing public awareness of environmental hazards has led to an increased
	demand for public health authorities to investigate geographical
	clustering of diseases. Although such cluster analysis is nearly
	always ineffective in identifying causes of disease, it often has
	to be used to address public concern about environmental hazards.
	Interpreting the resulting data is not straightforward, however,
	and this paper presents a guide for the non-specialist. The pitfalls
	include the fact that cluster analyses are usually done post hoc,
	and not as a result of a prior hypothesis. This is particularly true
	for investigations prompted by reported clusters, which have the
	inherent danger of overestimating the disease rate through "boundary
	shrinkage" of the population from which the cases are assumed to
	have arisen. In disease surveillance the problem of making multiple
	comparisons can be overcome by testing for clustering and autocorrelation.
	When rates of disease are illustrated in disease maps undue focus
	on areas where random fluctuation is greatest can be minimised by
	smoothing techniques. Despite the fact that cluster analyses rarely
	prove fruitful in identifying causation, they may—like single case
	reports—have the potential to generate new knowledge.}},
  citeulike-article-id = {8658324},
  citeulike-linkout-0 = {http://dx.doi.org/10.2307/29733060},
  citeulike-linkout-1 = {http://www.jstor.org/stable/29733060},
  doi = {10.2307/29733060},
  issn = {09598138},
  keywords = {cluster, cluster\_detection, disease, health},
  owner = {ijt1},
  posted-at = {2011-01-20 16:51:26},
  priority = {2},
  publisher = {BMJ Publishing Group},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.2307/29733060}
}

@INBOOK{gamk96,
  chapter = {Using a geographical analysis machine to detect the presence of spatial
	clusters and the location of clusters in synthetic data},
  pages = {68-87},
  title = {Methods for Investigating Localised Clustering of Disease},
  publisher = {IARC Scientific Publication},
  year = {1996},
  editor = {F. E. Alexander and P. Boyle},
  author = {S. Openshaw},
  number = {135},
  address = {Lyon, France},
  owner = {ijt1},
  timestamp = {2011.01.27}
}

@ARTICLE{citeulike:623869,
  author = {Openshaw, Stan},
  title = {{Developing Automated and Smart Spatial Pattern Exploration Tools
	for Geographical Information Systems Applications}},
  journal = {Journal of the Royal Statistical Society. Series~D (The Statistician)},
  year = {1995},
  volume = {44},
  number = {1},
  abstract = {{The paper examines some of the problems that users of geographical
	information systems (GISs) face in attempting to perform spatial
	analysis. A case is made for the development of new types of smart
	exploratory analysis tools able to explore spatial data effectively
	while also coping with the problems associated with the data and
	the skill levels of the end-users. Some suggestions are made about
	how artificial intelligence methods borrowed from artificial life
	can be used to create spatial pattern hunting creatures that may
	provide the basis for more effective spatial analysis procedures
	for the use with GISs.}},
  citeulike-article-id = {623869},
  citeulike-linkout-0 = {http://dx.doi.org/10.2307/2348611},
  citeulike-linkout-1 = {http://www.jstor.org/stable/2348611},
  doi = {10.2307/2348611},
  issn = {00390526},
  keywords = {clustering, epidemiology, gam, gis, health},
  posted-at = {2008-02-07 15:00:10},
  priority = {2},
  publisher = {Blackwell Publishing for the Royal Statistical Society},
  url = {http://dx.doi.org/10.2307/2348611}
}

@ARTICLE{citeulike:5207314,
  author = {Openshaw, Stan and Charlton, Martin and Wymer, Colin and Craft, Alan},
  title = {{A Mark 1 Geographical Analysis Machine for the automated analysis
	of point data sets}},
  journal = {International Journal of Geographical Information Systems},
  year = {1987},
  volume = {1},
  pages = {335--358},
  number = {4},
  abstract = {{This paper presents the first of a new generation of spatial analytical
	technology based on a fusion of statistical, GIS and computational
	thinking. It describes how to build what is termed a Geographical
	Analysis Machine (GAM), with high descriptive power. A GAM offers
	an imaginative new approach to the analysis of point pattern data
	based on a fully automated process whereby a point data set is explored
	for evidence of pattern without being unduly affected by predefined
	areal units or data error. No prior information or specification
	of particular location-specific hypotheses is required. If geographical
	data contain strong evidence of pattern in geographical space, then
	the GAM will find it. This technology is demonstrated by an analysis
	of data on cancer for northern England.}},
  citeulike-article-id = {5207314},
  citeulike-linkout-0 = {http://dx.doi.org/10.1080/02693798708927821},
  doi = {10.1080/02693798708927821},
  keywords = {clustering, gam, health},
  posted-at = {2011-01-05 21:49:34},
  priority = {5},
  publisher = {Taylor \& Francis},
  url = {http://dx.doi.org/10.1080/02693798708927821}
}

@ARTICLE{Openshaw2001,
  author = {Openshaw, Stan and Turton, Ian},
  title = {{Using a Geographical Explanations Machine to Explore Spatial Factors
	relating to Primary School Performance}},
  journal = {Geographical and Environmental Modelling},
  year = {2001},
  volume = {5},
  pages = {85--101},
  number = {1},
  abstract = {{The development and application of an automated geographical data
	explorer designed to look for potentially interesting geographical
	associations in a GIS database without any prior hypotheses as to
	what to look for or where they may be applied are described. The
	method is briefly described and then demonstrated via a case study.
	This case study examines how the geographical variations in primary
	school performance in northern England can be related to other variables.
	Finally, suggestions are made for its further development by the
	addition of a smart search capability.}},
  citeulike-article-id = {4779866},
  citeulike-linkout-0 = {http://dx.doi.org/10.1080/13615930120032635},
  doi = {10.1080/13615930120032635},
  keywords = {explanation, exploration, gam, gem},
  owner = {ijt1},
  posted-at = {2009-06-08 18:37:08},
  priority = {0},
  publisher = {Routledge},
  timestamp = {2011.01.19},
  url = {http://dx.doi.org/10.1080/13615930120032635}
}

@ARTICLE{citeulike:1067467,
  author = {Openshaw, Stan and Turton, Ian},
  title = {{A parallel Kohonen algorithm for the classification of large spatial
	datasets}},
  journal = {Computers \& Geosciences},
  year = {1996},
  volume = {22},
  pages = {1019--1026},
  number = {9},
  month = {November},
  abstract = {{The paper describes the development of Kohonen-net-based methods
	suitable for the classification of large spatial datasets suitable
	for parallel processing. Parallelising the Kohonen net is not easy
	because the degree of natural parallelism is finely grained. This
	paper presents a new algorithm and demonstrates its performance on
	the Cray T3D parallel supercomputer.}},
  booktitle = {Neural Network Applications in the Geosciences},
  citeulike-article-id = {1067467},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0098-3004(96)00040-4},
  citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V7D-3VWKMT7-T/2/ce2c95078a6cceba269b0bd8bc334ae1},
  doi = {10.1016/S0098-3004(96)00040-4},
  keywords = {algorithm, classification, clustering, data, data-mining, geocomputation,
	geospatial},
  posted-at = {2007-01-25 20:10:05},
  priority = {0},
  url = {http://dx.doi.org/10.1016/S0098-3004(96)00040-4}
}

@ARTICLE{Openshaw1999a,
  author = {Openshaw, S. and Turton, I. and Macgill, J},
  title = {Using the Geographical Analysis Machine to Analyze Limiting Long-term
	Illness},
  journal = {Geographical and Environmental Modelling},
  year = {1999},
  volume = {3.1},
  pages = {83-99},
  owner = {ijt1},
  timestamp = {2010.09.27}
}

@INCOLLECTION{citeulike:8468480,
  author = {Openshaw, Stan and Turton, Ian and Macgill, James and Davy, John},
  title = {{Putting the Geographical Analysis Machine on the Internet}},
  booktitle = {Innovations in GIS 6},
  publisher = {Taylor and Francis},
  year = {1999},
  editor = {Gittings, Bruce},
  chapter = {10},
  pages = {121--132},
  address = {London},
  abstract = {{Introduction Currently most of the proprietary GIS software systems
	lack sophisticated geographical analysis technology. Attempts over
	the last decade to persuade the system developers to add spatial
	analysis functionality has so far failed to have much visible impact.
	The traditional arguments seemingly still apply; viz. no strong market
	demand, fear of statistical complexity, lack of suitable GIS-able
	methods, and a deficiency of statistical skills amongst the GIS system
	developers. Various solutions to this dilemma have been suggested
	and tried out; in particular the development of spatial statistical
	add-ons tied to this or that GIS and the development of standalone
	statistical packages with either basic GIS functionality or easy
	linkage to one or more GIS systems. The problem is that these systems
	mainly serve research needs whereas most of the potential end-users
	of geographical analysis methods are not researchers in academia
	but involve the far larger numbers of global}},
  citeulike-article-id = {8468480},
  citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.6133},
  keywords = {clustering, gam, spatial, web, web\_gis},
  posted-at = {2010-12-22 18:54:02},
  priority = {2},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.6133}
}

@ARTICLE{citeulike:600780,
  author = {Otoo, Ekow J. and Shoshani, Arie and Hwang, Seung-Won},
  title = {{Clustering High Dimensional Massive Scientific Datasets}},
  journal = {J. Intell. Inf. Syst.},
  year = {2001},
  volume = {17},
  pages = {147--168},
  number = {2-3},
  address = {Hingham, MA, USA},
  citeulike-article-id = {600780},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=607610},
  issn = {0925-9902},
  keywords = {clustering, data-mining},
  posted-at = {2006-04-26 02:02:33},
  priority = {2},
  publisher = {Kluwer Academic Publishers},
  url = {http://portal.acm.org/citation.cfm?id=607610}
}

@ARTICLE{citeulike:1997384,
  author = {Ozonoff, Al and Jeffery, Caroline and Manjourides, Justin and White,
	Laura F. and Pagano, Marcello},
  title = {{Effect of spatial resolution on cluster detection: a simulation
	study}},
  journal = {International Journal of Health Geographics},
  year = {2007},
  volume = {6},
  pages = {52+},
  month = {November},
  citeulike-article-id = {1997384},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-6-52},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18042281},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18042281},
  day = {27},
  doi = {10.1186/1476-072X-6-52},
  issn = {1476-072X},
  keywords = {clustering, health, simulation, spatial},
  posted-at = {2007-11-28 22:50:37},
  priority = {3},
  url = {http://dx.doi.org/10.1186/1476-072X-6-52}
}

@ARTICLE{citeulike:2349265,
  author = {Ozonoff, Al and Webster, Thomas and Vieira, Veronica and Weinberg,
	Janice and Ozonoff, David and Aschengrau, Ann},
  title = {{Cluster detection methods applied to the Upper Cape Cod cancer data}},
  journal = {Environmental Health: A Global Access Science Source},
  year = {2005},
  volume = {4},
  number = {1},
  abstract = {{BACKGROUND:A variety of statistical methods have been suggested to
	assess the degree and/or the location of spatial clustering of disease
	cases. However, there is relatively little in the literature devoted
	to comparison and critique of different methods. Most of the available
	comparative studies rely on simulated data rather than real data
	sets.METHODS:We have chosen three methods currently used for examining
	spatial disease patterns: the M-statistic of Bonetti and Pagano;
	the Generalized Additive Model (GAM) method as applied by Webster;
	and Kulldorff's spatial scan statistic. We apply these statistics
	to analyze breast cancer data from the Upper Cape Cancer Incidence
	Study using three different latency assumptions.RESULTS:The three
	different latency assumptions produced three different spatial patterns
	of cases and controls. For 20 year latency, all three methods generally
	concur. However, for 15 year latency and no latency assumptions,
	the methods produce different results when testing for global clustering.CONCLUSION:The
	comparative analyses of real data sets by different statistical methods
	provides insight into directions for further research. We suggest
	a research program designed around examining real data sets to guide
	focused investigation of relevant features using simulated data,
	for the purpose of understanding how to interpret statistical methods
	applied to epidemiological data with a spatial component.}},
  citeulike-article-id = {2349265},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-069X-4-19},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/16164750},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=16164750},
  doi = {10.1186/1476-069X-4-19},
  keywords = {clustering, epidemiology, gam, health, satscan},
  posted-at = {2008-02-07 14:33:01},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-069X-4-19}
}

@ARTICLE{citeulike:474616,
  author = {Pei, Tao and Zhu, Axing and Zhou, Chenghu and Li, Baolin and Qin,
	Chengzhi},
  title = {{A new approach to the nearestneighbour method to discover cluster
	features in overlaid spatial point processes}},
  journal = {International Journal of Geographical Information Science},
  year = {2006},
  volume = {20},
  pages = {153--168},
  number = {2},
  citeulike-article-id = {474616},
  citeulike-linkout-0 = {http://dx.doi.org/10.1080/13658810500399654},
  citeulike-linkout-1 = {http://www.ingentaconnect.com/content/tandf/tgis/2006/00000020/00000002/art00003},
  doi = {10.1080/13658810500399654},
  issn = {1365-8816},
  keywords = {cluster, clustering, data-mining, geospatial},
  posted-at = {2006-07-21 18:59:26},
  priority = {2},
  publisher = {Taylor and Francis Ltd},
  url = {http://dx.doi.org/10.1080/13658810500399654}
}

@ARTICLE{citeulike:5732058,
  author = {Pei, Tao and Zhu, A-Xing and Zhou, Chenghu and Li, Baolin and Qin,
	Chengzhi},
  title = {{Detecting feature from spatial point processes using Collective
	Nearest Neighbor}},
  journal = {Computers, Environment and Urban Systems},
  year = {2009},
  month = {September},
  citeulike-article-id = {5732058},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.compenvurbsys.2009.08.001},
  citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0198971509000660},
  day = {05},
  doi = {10.1016/j.compenvurbsys.2009.08.001},
  issn = {01989715},
  keywords = {clustering, point, spatial, spatial\_point\_processes},
  posted-at = {2009-09-08 01:37:23},
  priority = {2},
  url = {http://dx.doi.org/10.1016/j.compenvurbsys.2009.08.001}
}

@ARTICLE{citeulike:1463384,
  author = {Porter, Michael D. and Brown, Donald E.},
  title = {{Detecting local regions of change in high-dimensional criminal or
	terrorist point processes}},
  journal = {Computational Statistics \& Data Analysis},
  year = {2007},
  volume = {51},
  pages = {2753--2768},
  number = {5},
  month = {February},
  abstract = {{A method is presented for detecting changes to the distribution of
	a criminal or terrorist point process between two time periods using
	a non-model-based approach. By treating the criminal/terrorist point
	process as an intelligent site selection problem, changes to the
	process can signify changes in the behavior or activity level of
	the criminals/terrorists. The locations of past events and an associated
	vector of geographic, environmental, and socio-economic feature values
	are employed in the analysis. By modeling the locations of events
	in each time period as a marked point process, we can then detect
	differences in the intensity of each component process. A modified
	PRIM (patient rule induction method) is implemented to partition
	the high-dimensional feature space, which can include mixed variables,
	into the most likely change regions. Monte Carlo simulations are
	easily and quickly generated under random relabeling to test a scan
	statistic for significance. By detecting local regions of change,
	not only can it be determined if change has occurred in the study
	area, but the specific spatial regions where change occurs is also
	identified. An example is provided of breaking and entering crimes
	over two-time periods to demonstrate the use of this technique for
	detecting local regions of change. This methodology also applies
	to detecting regions of differences between two types of events such
	as in case-control data.}},
  citeulike-article-id = {1463384},
  citeulike-linkout-0 = {http://www.sciencedirect.com/science/article/B6V8V-4KGPMYT-1/2/ca8a15b61de3715122fa9f4012732bc5},
  day = {1},
  keywords = {algorithm, automated, clustering, terrorism},
  posted-at = {2007-07-17 18:47:25},
  priority = {2},
  url = {http://www.sciencedirect.com/science/article/B6V8V-4KGPMYT-1/2/ca8a15b61de3715122fa9f4012732bc5}
}

@ARTICLE{citeulike:6582099,
  author = {Rajaram, Satwik and Oono, Yoshi},
  title = {{NeatMap - non-clustering heat map alternatives in R}},
  journal = {BMC Bioinformatics},
  year = {2010},
  volume = {11},
  pages = {45+},
  number = {1},
  month = {January},
  abstract = {{BACKGROUND:The clustered heat map is the most popular means of visualizing
	genomic data. It compactly displays a large amount of data in an
	intuitive format that facilitates the detection of hidden structures
	and relations in the data. However, it is hampered by its use of
	cluster analysis which does not always respect the intrinsic relations
	in the data, often requiring non-standardized reordering of rows/columns
	to be performed post-clustering. This sometimes leads to uninformative
	and/or misleading conclusions. Often it is more informative to use
	dimension-reduction algorithms (such as Principal Component Analysis
	and Multi-Dimensional Scaling) which respect the topology inherent
	in the data. Yet, despite their proven utility in the analysis of
	biological data, they are not as widely used. This is at least partially
	due to the lack of user-friendly visualization methods with the visceral
	impact of the heat map.RESULTS:NeatMap is an R package designed to
	meet this need. NeatMap offers a variety of novel plots (in 2 and
	3 dimensions) to be used in conjunction with these dimension-reduction
	techniques. Like the heat map, but unlike traditional displays of
	such results, it allows the entire dataset to be displayed while
	visualizing relations between elements. It also allows superimposition
	of cluster analysis results for mutual validation. NeatMap is shown
	to be more informative than the traditional heat map with the help
	of two well-known microarray datasets.CONCLUSIONS:NeatMap thus preserves
	many of the strengths of the clustered heat map while addressing
	some of its deficiencies. It is hoped that NeatMap will spur the
	adoption of non-clustering dimension-reduction algorithms.}},
  citeulike-article-id = {6582099},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-11-45},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/20096121},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=20096121},
  day = {22},
  doi = {10.1186/1471-2105-11-45},
  issn = {1471-2105},
  keywords = {clustering, heatmap, r, statistics},
  posted-at = {2010-03-31 15:24:59},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1471-2105-11-45}
}

@MISC{citeulike:407641,
  author = {Ramos, Vitorino and Abraham, Ajith},
  title = {{Swarms on Continuous Data}},
  abstract = {{While being it extremely important, many Exploratory Data Analysis
	(EDA [21]) systems have the inhability to perform classification
	and visualization in a continuous basis or to self-organize new data-items
	into the older ones (evenmore into new labels if necessary), which
	can be crucial in KDD - Knowledge Discovery [10,1], Retrieval and
	Data Mining Systems [15,10] (interactive and online forms of Web
	Applications are just one example). This disadvantge is also present
	in more recent approaches ...}},
  citeulike-article-id = {407641},
  citeulike-linkout-0 = {http://alfa.ist.utl.pt/\~{}cvrm/staff/vramos/ref\_45.html},
  citeulike-linkout-1 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.15.4056},
  keywords = {adaptive\_computation, ant\_colony\_systems, artificial\_intelligence,
	artificial\_life, clustering, collective\_computing, collective\_intelligence,
	collective\_systems, complex\_systems, data-mining, distributed\_computing,
	self-organization, swarm\_intelligence, swarms},
  posted-at = {2006-04-22 21:37:11},
  priority = {2},
  url = {http://alfa.ist.utl.pt/\~{}cvrm/staff/vramos/ref\_45.html}
}

@ARTICLE{citeulike:501640,
  author = {Rivas, A. L. and Kunsberg, B. and Chowell, G. and Smith, S. D. and
	Hyman, J. M. and Schwager, S. J.},
  title = {{Human-mediated Foot-and-mouth Disease Epidemic Dispersal: Disease
	and Vector Clusters}},
  journal = {Journal of Veterinary Medicine, Series B},
  year = {2006},
  volume = {53},
  pages = {1--10},
  number = {1},
  month = {February},
  citeulike-article-id = {501640},
  citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1439-0450.2006.00904.x},
  citeulike-linkout-1 = {http://www.ingentaconnect.com/content/bsc/jvb/2006/00000053/00000001/art00001},
  citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/16460349},
  citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=16460349},
  doi = {10.1111/j.1439-0450.2006.00904.x},
  issn = {0931-1793},
  keywords = {cluster, clustering, disease, epidemiology, space, space-time, time},
  posted-at = {2007-04-02 19:10:07},
  priority = {3},
  publisher = {Blackwell Publishing},
  url = {http://dx.doi.org/10.1111/j.1439-0450.2006.00904.x}
}

@ARTICLE{citeulike:6854836,
  author = {Robertson, Colin and Nelson, Trisalyn},
  title = {{Review of software for space-time disease surveillance}},
  journal = {International Journal of Health Geographics},
  year = {2010},
  volume = {9},
  pages = {16+},
  month = {March},
  abstract = {{Disease surveillance makes use of information technology at almost
	every stage of the process, from data collection and collation, through
	to analysis and dissemination. Automated data collection systems
	enable near-real time analysis of incoming data. This context places
	a heavy burden on software used for space-time surveillance. In this
	paper, we review software programs capable of space-time disease
	surveillance analysis, and outline some of their salient features,
	shortcomings, and usability. Programs with space-time methods were
	selected for inclusion, limiting our review to ClusterSeer, SaTScan,
	GeoSurveillance and the Surveillance package for R. We structure
	the review around stages of analysis: preprocessing, analysis, technical
	issues, and output. Simulated data were used to review each of the
	software packages. SaTScan was found to be the best equipped package
	for use in an automated surveillance system. ClusterSeer is more
	suited to data exploration, and learning about the different methods
	of statistical surveillance.}},
  citeulike-article-id = {6854836},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-9-16},
  day = {12},
  doi = {10.1186/1476-072X-9-16},
  issn = {1476-072X},
  keywords = {clustering, disease, health, spatiotemporal, statistical},
  posted-at = {2010-03-16 19:34:22},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-072X-9-16}
}

@ARTICLE{citeulike:2349222,
  author = {Sabel, Clive E. and Gatrell, Anthony C. and Loytonen, Markku and
	Maasilta, Paula and Jokelainen, Matti},
  title = {{Modelling exposure opportunities: estimating relative risk for motor
	neurone disease in Finland}},
  journal = {Social Science \& Medicine},
  year = {2000},
  volume = {50},
  pages = {1121--1137},
  number = {7-8},
  month = {April},
  abstract = {{This paper addresses the issues surrounding an individual's exposure
	to potential environmental risk factors, which can be implicated
	in the aetiology of a disease. We hope to further elucidate the `lag'
	or latency period between the initial exposure to potential pathogens
	and the physical emergence of the disease, with specific reference
	to the rare neurological condition, motor neurone disease (MND),
	using a dataset obtained from the Finnish Death Certificate registry,
	for MND deaths between the period 1985-1995. A space-time approach
	is adopted, whereby patterns in both time and space are considered.
	No prior assumptions about the aetiology of MND are adopted. By using
	methods for the analysis of point processes, which preserve the continuous
	nature of the data, we resolve some of the problems of analysis that
	are often based on arbitrary areal units, such as postcode boundaries,
	or political boundaries. We use kernel estimation to model space-time
	patterns. Raised relative risk is assessed by adopting appropriate
	adjustments for the underlying population at risk, with the use of
	controls. Significance of the results is assessed using Monte Carlo
	simulation, and comparisons are made with results obtained from Openshaw's
	geographical analysis machine (GAM). Our results demonstrate the
	utility of kernel estimation as a visualisation tool. Small areas
	of elevated risk are identified, which need to be more closely examined
	before any firm conclusions can be drawn. We highlight a number of
	issues concerning the inadequacies of the data, and possibly of the
	techniques themselves.}},
  citeulike-article-id = {2349222},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0277-9536(99)00360-3},
  citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6VBF-3YDG0NP-M/2/f73ab78b6c511a5029fedaee828ed665},
  day = {1},
  doi = {10.1016/S0277-9536(99)00360-3},
  keywords = {clustering, epidemiology, gam, health},
  posted-at = {2008-02-07 14:21:36},
  priority = {0},
  url = {http://dx.doi.org/10.1016/S0277-9536(99)00360-3}
}

@ARTICLE{citeulike:2839659,
  author = {Santamaria, Rodrigo and Theron, Roberto and Quintales, Luis},
  title = {{A visual analytics approach for understanding biclustering results
	from microarray data}},
  journal = {BMC Bioinformatics},
  year = {2008},
  volume = {9},
  pages = {247+},
  number = {1},
  month = {May},
  abstract = {{BACKGROUND:Microarray analysis is an important area of bioinformatics.
	In the last few years, biclustering has become one of the most popular
	methods for classifying data from microarrays. Although biclustering
	can be used in any kind of classification problem, nowadays it is
	mostly used for microarray data classification. A large number of
	biclustering algorithms have been developed over the years, however
	little effort has been devoted to the representation of the results.RESULTS:We
	present an interactive framework that helps to infer differences
	or similarities between biclustering results, to unravel trends and
	to highlight robust groupings of genes and conditions. These linked
	representations of biclusters can complement biological analysis
	and reduce the time spent by specialists on interpreting the results.
	Within the framework, besides other standard representations, a visualization
	technique is presented which is based on a force-directed graph where
	biclusters are represented as flexible overlapped groups of genes
	and conditions. This microarray analysis framework (BicOverlapper),
	is available at http://vis.usal.es/bicoverlapperCONCLUSION:The main
	visualization technique, tested with different biclustering results
	on a real dataset, allows researchers to extract interesting features
	of the biclustering results, especially the highlighting of overlapping
	zones that usually represent robust groups of genes and/or conditions.
	The visual analytics methodology will permit biology experts to study
	biclustering results without inspecting an overwhelming number of
	biclusters individually.}},
  citeulike-article-id = {2839659},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-9-247},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18505552},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18505552},
  day = {27},
  doi = {10.1186/1471-2105-9-247},
  issn = {1471-2105},
  keywords = {clustering, infovis, statistical, visualization},
  posted-at = {2008-08-04 15:02:51},
  priority = {3},
  url = {http://dx.doi.org/10.1186/1471-2105-9-247}
}

@BOOK{citeulike:1610419,
  title = {{Programming Collective Intelligence: Building Smart Web 2.0 Applications}},
  publisher = {O'Reilly Media},
  year = {2007},
  author = {Segaran, Toby},
  edition = {1},
  month = {August},
  abstract = {{Want to tap the power behind search rankings, product recommendations,
	social bookmarking, and online matchmaking? This fascinating book
	demonstrates how you can build Web 2.0 applications to mine the enormous
	amount of data created by people on the Internet. With the sophisticated
	algorithms in this book, you can write smart programs to access interesting
	datasets from other web sites, collect data from users of your own
	applications, and analyze and understand the data once you've found
	it. \_Programming Collective Intelligence\_ takes you into the world
	of machine learning and statistics, and explains how to draw conclusions
	about user experience, marketing, personal tastes, and human behavior
	in general--all from information that you and others collect every
	day. Each algorithm is described clearly and concisely with code
	that can immediately be used on your web site, blog, Wiki, or specialized
	application. This book explains:
	
	 * Collaborative filtering techniques that enable online retailers
	to recommend products or media
	
	 * Methods of clustering to detect groups of similar items in a large
	dataset
	
	 * Search engine features--crawlers, indexers, query engines, and
	the PageRank algorithm
	
	 * Optimization algorithms that search millions of possible solutions
	to a problem and choose the best one
	
	 * Bayesian filtering, used in spam filters for classifying documents
	based on word types and other features
	
	 * Using decision trees not only to make predictions, but to model
	the way decisions are made
	
	 * Predicting numerical values rather than classifications to build
	price models
	
	 * Support vector machines to match people in online dating sites
	
	 * Non-negative matrix factorization to find the independent features
	in adataset
	
	 * Evolving intelligence for problem solving--how a computer develops
	its skill by improving its own code the more it plays a gameÂ 
	
	Each chapter includes exercises for extending the algorithms to make
	them more powerful. Go beyond simple database-backed applications
	and put the wealth of Internet data to work for you.
	
	
	"Bravo! I cannot think of a better way for a developer to first learn
	these algorithms and methods, nor can I think of a better way for
	me (an old AI dog) to reinvigorate my knowledge of the details."
	
	-- Dan Russell, Google
	
	
	"Toby's book does a great job of breaking down the complex subject
	matter of machine-learning algorithms into practical, easy-to-understand
	examples that can be directly applied to analysis of social interaction
	across the Web today. If I had this book two years ago, it would
	have saved precious time going down some fruitless paths."
	
	-- Tim Wolters, CTO, Collective Intellect}},
  citeulike-article-id = {1610419},
  citeulike-linkout-0 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&amp;path=ASIN/0596529325},
  citeulike-linkout-1 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21\&amp;path=ASIN/0596529325},
  citeulike-linkout-2 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21\&amp;path=ASIN/0596529325},
  citeulike-linkout-3 = {http://www.amazon.jp/exec/obidos/ASIN/0596529325},
  citeulike-linkout-4 = {http://www.amazon.co.uk/exec/obidos/ASIN/0596529325/citeulike00-21},
  citeulike-linkout-5 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/0596529325},
  citeulike-linkout-6 = {http://www.worldcat.org/isbn/0596529325},
  citeulike-linkout-7 = {http://books.google.com/books?vid=ISBN0596529325},
  citeulike-linkout-8 = {http://www.amazon.com/gp/search?keywords=0596529325\&index=books\&linkCode=qs},
  citeulike-linkout-9 = {http://www.librarything.com/isbn/0596529325},
  day = {16},
  howpublished = {Paperback},
  isbn = {0596529325},
  keywords = {agents, ai, algorithm, bayesian, clustering, collective\_computing,
	geneticalgorithms, hacks, information-systems, toolkit, web20},
  posted-at = {2007-10-11 18:43:46},
  priority = {0},
  url = {http://www.worldcat.org/isbn/0596529325}
}

@ARTICLE{Selvin1988,
  author = {Selvin, S.},
  title = {{Transformations of maps to investigate clusters of disease}},
  journal = {Social Science \& Medicine},
  year = {1988},
  volume = {26},
  pages = {215--221},
  number = {2},
  abstract = {{An approach is presented to display and analyze epidemiologic data
	using population density equalized maps (cartograms). The algorithm
	for generating these maps is discussed. A specific method for statistically
	analyzing plotted data is given, followed by an application of maps
	and analysis to 73 sets of age-, race-, sex-, and site-specific cancer
	incidence data. The data were obtained from the Surveillance, Epidemiology
	and End Results project for San Francisco City/County (1978–1981)
	and combined with 1980 U.S. Census data.}},
  citeulike-article-id = {7258408},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/0277-9536(88)90242-0},
  citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/0277-9536(88)90242-0},
  doi = {10.1016/0277-9536(88)90242-0},
  issn = {02779536},
  keywords = {cartogram, cluster, disease, health},
  owner = {ijt1},
  posted-at = {2010-11-05 14:32:53},
  priority = {2},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1016/0277-9536(88)90242-0}
}

@MISC{Smith,
  author = {Smith, Geoffrey H.},
  title = {{Disease Cluster Detection Methods: The Impact of Choice of Shape
	on the Power of Statistical Tests}},
  abstract = {{To effectively detect disease clusters, spatial analysis methods
	should identify clusters when they exist and reject them when they
	are false. The methods should as often as possible identify true
	clusters and not identify false clusters. Recent advances in cluster
	detection methods have relied on circles as the basic shape for these
	analyses. These methods have been tested on both actual and synthetic
	data, but not on a credible data set consisting of a non-circular
	cluster. The objective is to see if cluster detection methods based
	on circles can also detect such non-circular clusters. To test the
	ability of these methods to detect true clusters and not detect false
	ones, a synthetic data set was constructed. In a region with a non-uniform
	distribution of 8,689 people at-risk, 72 deaths were simulated both
	inside a sinuous cluster (1,020 people at-risk in a 0.5-mile buffer
	of a transportation route) as well as outside the cluster (the remaining
	7,669 people at-risk outside the buffer). Three methods for identifying
	disease clusters were tested on this data: Openshaw's GAM/K, Kulldorff
	and Nagarwalla's Spatial Scan Statistic, and Rushton and Lolonis'
	significance map. I will report the results of each method with respect
	to three key criteria: sensitivity, specificity, and positive predictive
	value. Because no method successfully identified the sinuous cluster,
	new methods for detecting non-circular clusters will be discussed.}},
  citeulike-article-id = {2349387},
  citeulike-linkout-0 = {http://www.cobblestoneconcepts.com/ucgis2summer/smith/SMITH.HTM},
  institution = {Department of Geography University of Iowa},
  keywords = {cluster, disease, epidemiology, gam, satscan},
  owner = {ijt1},
  posted-at = {2008-02-07 15:04:55},
  priority = {5},
  timestamp = {2011.01.19},
  url = {http://www.cobblestoneconcepts.com/ucgis2summer/smith/SMITH.HTM}
}

@INPROCEEDINGS{citeulike:1400940,
  author = {Stoica, E. and Hearst, M. and Richardson, M.},
  title = {{Automating Creation of Hierarchical Faceted Metadata Structures}},
  booktitle = {NAACL/HLT 2007},
  year = {2007},
  citeulike-article-id = {1400940},
  file = {Stoica2007Automating.pdf:Stoica2007Automating.pdf:PDF},
  keywords = {clustering, facets, knowledge-management, metadata},
  posted-at = {2007-12-03 23:07:41},
  priority = {2}
}

@PHDTHESIS{citeulike:600785,
  author = {Strehl, Alexander},
  title = {{Relationship-based clustering and cluster ensembles for high-dimensional
	data mining}},
  year = {2002},
  abstract = {{Supervisor-Joydeep Ghosh}},
  citeulike-article-id = {600785},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=959655},
  keywords = {clustering, data-mining},
  posted-at = {2006-04-26 02:01:43},
  priority = {2},
  url = {http://portal.acm.org/citation.cfm?id=959655}
}

@INPROCEEDINGS{citeulike:4186975,
  author = {Stryker, M. and Turton, Ian and Maceachren, A.},
  title = {Health {G}eo{J}unction: {G}eovisualization of news and scientific
	publications to support situation awareness},
  booktitle = {GIScience'08},
  year = {2008},
  address = {Park City, Utah},
  citeulike-article-id = {4186975},
  citeulike-linkout-0 = {http://geoanalytics.net/GeoVisualAnalytics08/a18.pdf},
  keywords = {citations, clustering, geographic, pubmed, webmapping},
  posted-at = {2009-03-17 16:48:38},
  priority = {0},
  url = {http://geoanalytics.net/GeoVisualAnalytics08/a18.pdf}
}

@ARTICLE{citeulike:2652155,
  author = {Takahashi, Kunihiko and Kulldorff, Martin and Tango, Toshiro and
	Yih, Katherine},
  title = {{A flexibly shaped space-time scan statistic for disease outbreak
	detection and monitoring}},
  journal = {International Journal of Health Geographics},
  year = {2008},
  volume = {7},
  pages = {14+},
  month = {April},
  citeulike-article-id = {2652155},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-7-14},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18402711},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18402711},
  day = {11},
  doi = {10.1186/1476-072X-7-14},
  issn = {1476-072X},
  keywords = {cluster, clustering, disease, health, space, space-time},
  posted-at = {2008-04-11 16:04:50},
  priority = {5},
  url = {http://dx.doi.org/10.1186/1476-072X-7-14}
}

@ARTICLE{citeulike:2316346,
  author = {Thorpe, Nancy and Shirmohammadi, Adel},
  title = {{Herbicides and Nitrates in Groundwater of Maryland and Childhood
	Cancers: A Geographic Information Systems Approach}},
  journal = {Journal of Environmental Science and Health, Part C},
  year = {2005},
  volume = {23},
  pages = {261--278},
  number = {2},
  abstract = {{This hypothesis-generating study explores spatial patterns of childhood
	cancers in Maryland and investigates their potential associations
	with herbicides and nitrates in groundwater. The Maryland Cancer
	Registry (MCR) provided data for bone and brain cancers, leukemia,
	and lymphoma, for ages 0\&ndash;17, during the years 1992\&ndash;1998.
	Cancer clusters and relative risks generated in the study indicate
	higher relative risk areas and potential clusters in several counties.
	Contingency table analysis indicates a potential association with
	several herbicides and nitrates. Cancer rates for the four types
	have a crude odds ratio (OR) = 1.10 (0.78\&ndash;1.56) in relationship
	to atrazine, and an OR = 1.54 (1.14\&ndash;2.07) for metolachlor.
	Potential association to mixtures of three compounds give an OR =
	7.56 (4.16\&ndash;13.73). A potential association is indicated between
	leukemia and nitrates, OR = 1.81 (1.35\&ndash;2.42), and bone cancer
	with metolachlor, OR = 2.26 (0.97\&ndash;5.24). These results give
	insight to generate a hypothesis of the potential association between
	exposure to these herbicides and nitrates and specific types of childhood
	cancer.}},
  citeulike-article-id = {2316346},
  citeulike-linkout-0 = {http://dx.doi.org/10.1080/10590500500235001},
  doi = {10.1080/10590500500235001},
  keywords = {cancer, clustering, epidemiology, geography, gis},
  posted-at = {2008-01-31 20:41:34},
  priority = {3},
  publisher = {Taylor \&amp; Francis},
  url = {http://dx.doi.org/10.1080/10590500500235001}
}

@ARTICLE{Torabi2008,
  author = {Torabi, Mahmoud and Rosychuk, Rhonda J.},
  title = {{Spatial event cluster detection using an approximate normal distribution}},
  journal = {International Journal of Health Geographics},
  year = {2008},
  volume = {7},
  pages = {61+},
  month = {December},
  abstract = {{Background:\&\#10;In geographic surveillance of disease, areas with
	large numbers of disease cases are to be identified so that investigations
	of the causes of high disease rates can be pursued. Areas with high
	rates are called disease clusters and statistical cluster detection
	tests are used to identify geographic areas with higher disease rates
	than expected by chance alone. Typically cluster detection tests
	are applied to incident or prevalent cases of disease, but surveillance
	of disease-related events, where an individual may have multiple
	events, may also be of interest. Previously, a compound Poisson approach
	that detects clusters of events by testing individual areas that
	may be combined with their neighbours has been proposed. However,
	the relevant probabilities from the compound Poisson distribution
	are obtained from a recursion relation that can be cumbersome if
	the number of events are large or analyses by strata are performed.
	We propose a simpler approach that uses an approximate normal distribution.
	This method is very easy to implement and is applicable to situations
	where the population sizes are large and the population distribution
	by important strata may differ by area. We demonstrate the approach
	on pediatric self-inflicted injury presentations to emergency departments
	and compare the results for probabilities based on the recursion
	and the normal approach. We also implement a Monte Carlo simulation
	to study the performance of the proposed approach.\&\#10;Results:\&\#10;In
	a self-inflicted injury data example, the normal approach identifies
	twelve out of thirteen of the same clusters as the compound Poisson
	approach, noting that the compound Poisson method detects twelve
	significant clusters in total. Through simulation studies, the normal
	approach well approximates the compound Poisson approach for a variety
	of different population sizes and case and event thresholds.\&\#10;Conclusion:\&\#10;A
	drawback of the compound Poisson approach is that the relevant probabilities
	must be determined through a recursion relation and such calculations
	can be computationally intensive if the cluster size is relatively
	large or if analyses are conducted with strata variables. On the
	other hand, the normal approach is very flexible, easily implemented,
	and hence, more appealing for users. Moreover, the concepts may be
	more easily conveyed to non-statisticians interested in understanding
	the methodology associated with cluster detection test results.}},
  citeulike-article-id = {3786172},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-7-61},
  day = {12},
  doi = {10.1186/1476-072X-7-61},
  issn = {1476-072X},
  keywords = {cluster, health, spatial},
  owner = {ijt1},
  posted-at = {2008-12-13 19:29:32},
  priority = {3},
  timestamp = {2011.01.20},
  url = {http://dx.doi.org/10.1186/1476-072X-7-61}
}

@INCOLLECTION{Hall2008,
  author = {Ian Turton},
  title = {Geo{T}ools},
  booktitle = {Open Source Approaches in Spatial Data Handling (Advances in Geographic
	Information Science)},
  publisher = {Springer},
  year = {2008},
  editor = {Hall, Brent G. and Leahy, Michael G.},
  edition = {1st},
  abstract = {This book focuses on the nature and characteristics of open source
	geospatial(OSG) software. TheÂ role of OSG approaches in spatial
	data handling is thecross-cutting theme of the book. Various sub-themes
	are explored are exploredthat introduce readers unfamiliar to OSG
	software to the nature, purpose andapplications of OS programming,
	and to the key new OS tools and theirapplication within the geospatial
	data domain. The book also includes adiscussion of new tools, approaches
	and applications for those already usingOS approaches to software
	development.},
  citeulike-article-id = {3501710},
  citeulike-linkout-0 = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&amp;path=ASIN/354074830X},
  citeulike-linkout-1 = {http://www.amazon.de/exec/obidos/redirect?tag=citeulike01-21\&amp;path=ASIN/354074830X},
  citeulike-linkout-2 = {http://www.amazon.fr/exec/obidos/redirect?tag=citeulike06-21\&amp;path=ASIN/354074830X},
  citeulike-linkout-3 = {http://www.amazon.jp/exec/obidos/ASIN/354074830X},
  citeulike-linkout-4 = {http://www.amazon.co.uk/exec/obidos/ASIN/354074830X/citeulike00-21},
  citeulike-linkout-5 = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/354074830X},
  citeulike-linkout-6 = {http://www.worldcat.org/isbn/354074830X},
  citeulike-linkout-7 = {http://books.google.com/books?vid=ISBN354074830X},
  citeulike-linkout-8 = {http://www.amazon.com/gp/search?keywords=354074830X\&index=books\&linkCode=qs},
  citeulike-linkout-9 = {http://www.librarything.com/isbn/354074830X},
  howpublished = {Hardcover},
  isbn = {354074830X},
  keywords = {geocomputation, geovistastudio, opensource},
  owner = {ijt1},
  posted-at = {2008-11-10 19:22:22},
  priority = {4},
  timestamp = {2011.01.20},
  url = {http://www.worldcat.org/isbn/354074830X}
}

@INBOOK{citeulike:2847357,
  pages = {87--102},
  title = {{Testing space-time and more complex hyperspace geographical analysis
	tools}},
  publisher = {Taylor and Francis},
  year = {2000},
  editor = {Atkinson, Peter M. and Martin, David},
  author = {Turton, I. and Openshaw, S. and Brunsdon, C. and Turner, A. and Macgill,
	J.},
  address = {London, UK},
  booktitle = {GIS and Geocomputation},
  citeulike-article-id = {2847357},
  citeulike-linkout-0 = {http://books.google.com/books?hl=en\&\#38;lr=\&\#38;id=SVbNsMklmOgC\&\#38;oi=fnd\&\#38;pg=PA87\&\#38;ots=JNe9dRTT4a\&\#38;sig=9NWVru2OGNlUds-7zQZFboq2mhQ},
  keywords = {analysis, clustering, exploratory, gam, geographic, space, space-time,
	testing, time},
  posted-at = {2008-05-30 14:54:18},
  priority = {0},
  url = {http://books.google.com/books?hl=en\&\#38;lr=\&\#38;id=SVbNsMklmOgC\&\#38;oi=fnd\&\#38;pg=PA87\&\#38;ots=JNe9dRTT4a\&\#38;sig=9NWVru2OGNlUds-7zQZFboq2mhQ}
}

@ARTICLE{citeulike:2889894,
  author = {Ugarte, M. D. and Goicoa, T. and Militino, A. F.},
  title = {{Empirical Bayes and Fully Bayes procedures to detect high-risk areas
	in disease mapping}},
  journal = {Computational Statistics \& Data Analysis},
  volume = {In Press, Accepted Manuscript},
  citeulike-article-id = {2889894},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.csda.2008.06.002},
  citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V8V-4SR7114-1/1/834980537976450b5295f057c9a52595},
  doi = {10.1016/j.csda.2008.06.002},
  keywords = {bayesian, clustering, geography, health, mapping, spatial},
  posted-at = {2008-06-12 19:50:14},
  priority = {2},
  url = {http://dx.doi.org/10.1016/j.csda.2008.06.002}
}

@ARTICLE{citeulike:2210011,
  author = {Vesanto, J. and Alhoniemi, E.},
  title = {{Clustering of the self-organizing map}},
  journal = {Neural Networks, IEEE Transactions on},
  year = {2000},
  volume = {11},
  pages = {586--600},
  number = {3},
  abstract = {{The self-organizing map (SOM) is an excellent tool in exploratory
	phase of data mining. It projects input space on prototypes of a
	low-dimensional regular grid that can be effectively utilized to
	visualize and explore properties of the data. When the number of
	SOM units is large, to facilitate quantitative analysis of the map
	and the data, similar units need to be grouped, i.e., clustered.
	In this paper, different approaches to clustering of the SOM are
	considered. In particular, the use of hierarchical agglomerative
	clustering and partitive clustering using K-means are investigated.
	The two-stage procedure-first using SOM to produce the prototypes
	that are then clustered in the second stage-is found to perform well
	when compared with direct clustering of the data and to reduce the
	computation time}},
  booktitle = {Neural Networks, IEEE Transactions on},
  citeulike-article-id = {2210011},
  citeulike-linkout-0 = {http://dx.doi.org/10.1109/72.846731},
  citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=846731},
  citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18249787},
  citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18249787},
  doi = {10.1109/72.846731},
  keywords = {clustering, data-mining, som},
  posted-at = {2008-01-10 19:23:53},
  priority = {3},
  url = {http://dx.doi.org/10.1109/72.846731}
}

@ARTICLE{citeulike:2874110,
  author = {Vichi, Maurizio and Saporta, Gilbert},
  title = {{Clustering and Disjoint Principal Component Analysis}},
  journal = {Computational Statistics \& Data Analysis},
  volume = {In Press, Accepted Manuscript},
  citeulike-article-id = {2874110},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.csda.2008.05.028},
  citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V8V-4SPC0JK-1/1/09acf6a1a6a7897e505a927a7a800f36},
  doi = {10.1016/j.csda.2008.05.028},
  keywords = {clustering, pca},
  posted-at = {2008-06-08 23:12:01},
  priority = {2},
  url = {http://dx.doi.org/10.1016/j.csda.2008.05.028}
}

@INCOLLECTION{citeulike:3081537,
  author = {Ward, Michael},
  title = {{Spatial Epidemiology: Where Have We Come in 150 Years?}},
  year = {2008},
  pages = {257--282},
  abstract = {{Modern epidemiology is founded on a tradition of spatial analysis.
	The genesis of this discipline can be traced to the classic work
	of John Snow and the Broad Street pump. During the 1850s, cholera
	outbreaks were an important cause of morbidity and mortality amongst
	the inhabitants of London. Using simple dot maps and visualization,
	Snow provided compelling evidence that cases were clustered and that
	fecal-contaminated drinking water might be the cause of some cholera
	outbreaks. During the intervening 150 years, spatial epidemiology
	(alternatively called landscape epidemiology and more broadly, medical
	geography) has developed into a field within its own right. During
	the past two decades, advances in geographic information systems
	and statistical methods for analyzing spatially-referenced health
	data has allowed epidemiologists to routinely perform spatial analyses.
	Some of the most beneficial advances in spatial epidemiology have
	been in the areas of data visualization, detection of disease clusters,
	identification of spatial risk factors, application of predictive
	models, and the routine incorporation of GIS into disease surveillance
	programs. In this chapter, approaches used in spatial epidemiology
	will be described. Some specific techniques that are currently popular
	in the discipline will be presented. Several case studies will be
	used to highlight the application of these techniques within the
	field of spatial epidemiology and to illustrate the potential value
	of this discipline to public health and homeland security. The chapter
	will conclude by considering some of the major obstacles that remain
	to the consolidation of spatial analysis as a foundation of modern
	epidemiology, including the availability and quality of spatial disease
	data, information on the distributions of the populations at-risk,
	and integration of methods seamlessly into epidemiologic software
	packages.}},
  citeulike-article-id = {3081537},
  citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-1-4020-8507-9\_13},
  doi = {10.1007/978-1-4020-8507-9\_13},
  journal = {Geospatial Technologies and Homeland Security},
  keywords = {clustering, epidemiology, geographic, historic, spatial},
  posted-at = {2008-08-04 15:08:30},
  priority = {3},
  url = {http://dx.doi.org/10.1007/978-1-4020-8507-9\_13}
}

@ARTICLE{citeulike:3081502,
  author = {Ward, M. P. and Carpenter, T. E.},
  title = {{Techniques for analysis of disease clustering in space and in time
	in veterinary epidemiology}},
  journal = {Preventive Veterinary Medicine},
  year = {2000},
  pages = {257--284},
  month = {June},
  abstract = {{Techniques to describe and investigate clustering of disease in space
	- the nearest-neighbour test, autocorrelation, Cuzick-and-Edwards'
	test and the spatial scan statistic - and in time - the Ederer-Myers-Mantel
	test and the temporal scan statistic - are reviewed. The application
	of these techniques in veterinary epidemiology is demonstrated by
	the analysis of a data set describing the occurrence of blowfly strike
	- both body strike and breech strike - between August 1998 and May
	1999 in 33 commercial sheep flocks located within two local government
	areas of southeastern Queensland, Australia. By applying a combination
	of these methods, the occurrence of blowfly strike in the study area
	is well-characterised in both space and time. Guidelines for investigating
	disease clusters in veterinary epidemiology are discussed.}},
  citeulike-article-id = {3081502},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0167-5877(00)00133-1},
  citeulike-linkout-1 = {http://www.ingentaconnect.com/content/els/01675877/2000/00000045/00000003/art00133},
  doi = {10.1016/S0167-5877(00)00133-1},
  issn = {0167-5877},
  keywords = {clustering, disease, epidemiology, health, space-time},
  posted-at = {2008-08-04 14:54:00},
  priority = {4},
  publisher = {Elsevier},
  url = {http://dx.doi.org/10.1016/S0167-5877(00)00133-1}
}

@ARTICLE{citeulike:3081512,
  author = {Ward, M. P. and Maftei, D. and Apostu, C. and Suru, A.},
  title = {{Geostatistical visualisation and spatial statistics for evaluation
	of the dispersion of epidemic highly pathogenic avian influenza subtype
	H5N1.}},
  journal = {Veterinary research},
  year = {2008},
  volume = {39},
  number = {3},
  abstract = {{The aim of this study was to evaluate a range of statistical and
	geostatistical methods for their usefulness in providing insights
	into how highly pathogenic avian influenza (HPAI) subtype H5N1 might
	spread through a national population of village poultry. The insights
	gained allow the generation of disease dispersion hypotheses. The
	case study data set consisted of 161 outbreaks of HPAI subtype H5N1
	in village poultry reported in Romania between October 2005 and June
	2006. Reports of village outbreaks (\%) occurred in three waves:
	October-December (14\%), February-March (16\%), and May-June (68\%).
	Risk mapping - based on variography and kriging - was used to visualize
	the evolution of the epidemic. Outbreaks first appeared in eastern
	and southern Romania, particularly within an area that forms part
	of the Danube River Delta. The largest phase of the epidemic affected
	villages in all parts of central, southern, and eastern Romania,
	but outbreaks were clustered in central Romania. Outbreaks spread
	in an east to west direction. By using geostatistical visualisation
	and spatial statistics, the evolution of the epidemic could be characterised
	into two parts: disease introduction, local spread, and sporadic
	outbreaks, and long-distance disease spread with rapid epidemic propagation.
	This is consistent with the hypothesis that the environment and landscape
	(specifically the Danube River Delta) played a critical role in the
	introduction and initial spread of HPAI subtype H5N1 during the autumn
	and winter of 2005, and that the movement of poultry might have introduced
	the infection into central Romania during the spring and summer of
	2006. Further research focusing on the spatio-temporal interface
	between the two parts of the epidemic might reveal how and why it
	progressed from a confined, local epidemic to a large, national epidemic.
	Such information would assist efforts to limit the global spread
	of HPAI subtype H5N1.}},
  address = {College of Veterinary Medicine \& Biomedical Sciences, Texas A\&M
	University, College Station, Texas, USA. mward@cvm.tamu.edu},
  citeulike-article-id = {3081512},
  citeulike-linkout-0 = {http://dx.doi.org/10.1051/vetres:2007063},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18252188},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18252188},
  doi = {10.1051/vetres:2007063},
  issn = {0928-4249},
  keywords = {avianflu, clustering, disease, epidemiology, geovisualization, spatial,
	statistical, visualization},
  posted-at = {2008-08-04 14:59:28},
  priority = {4},
  url = {http://dx.doi.org/10.1051/vetres:2007063}
}

@ARTICLE{citeulike:3327388,
  author = {Warden, Craig},
  title = {{Comparison of Poisson and Bernoulli spatial cluster analyses of
	pediatric injuries in a fire district}},
  journal = {International Journal of Health Geographics},
  year = {2008},
  volume = {7},
  pages = {51+},
  number = {1},
  month = {September},
  abstract = {{BACKGROUND:With limited resources available, injury prevention efforts
	need to be targeted both geographically and to specific populations.
	As part of a pediatric injury prevention project, data was obtained
	on all pediatric medical and injury incidents in a fire district
	to evaluate geographical clustering of pediatric injuries. This will
	be the first step in attempting to prevent these injuries with specific
	interventions depending on locations and mechanisms.RESULTS:There
	were a total of 4803 incidents involving patients less than 15 years
	of age that the fire district responded to during 2001-2005 of which
	1997 were categorized as injuries and 2806 as medical calls. The
	two cohorts (injured versus medical) differed in age distribution
	(7.7 +/- 4.4 years versus 5.4 +/- 4.8 years, p < 0.001) and location
	type of incident (school or church 12\% versus 15\%, multifamily
	residence 22\% versus 13\%, single family residence 51\% versus 28\%,
	sport, park or recreational facility 3\% versus 8\%, public building
	8\% versus 7\%, and street or road 3\% versus 30\%, respectively,
	p < 0.001). Using the medical incident locations as controls, there
	was no significant clustering for environmental or assault injuries
	using the Bernoulli method while there were four significant clusters
	for all injury mechanisms combined, 13 clusters for motor vehicle
	collisions, one for falls, and two for pedestrian or bicycle injuries.
	Using the Poisson cluster method on incidence rates by census tract
	identified four clusters for all injuries, three for motor vehicle
	collisions, four for fall injuries, and one each for environmental
	and assault injuries. The two detection methods shared a minority
	of overlapping geographical clusters.CONCLUSION:Significant clustering
	occurs overall for all injury mechanisms combined and for each mechanism
	depending on the cluster detection method used. There was some overlap
	in geographic clusters identified by both methods. The Bernoulli
	method allows more focused cluster mapping and evaluation since it
	directly uses location data. Once clusters are found, interventions
	can be targeted to specific geographic locations, location types,
	ages of victims, and mechanisms of injury.}},
  citeulike-article-id = {3327388},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-7-51},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18808720},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18808720},
  day = {22},
  doi = {10.1186/1476-072X-7-51},
  issn = {1476-072X},
  keywords = {clustering, health},
  posted-at = {2008-09-24 18:00:45},
  priority = {2},
  url = {http://dx.doi.org/10.1186/1476-072X-7-51}
}

@ARTICLE{citeulike:1190374,
  author = {Wheeler, David},
  title = {{A comparison of spatial clustering and cluster detection techniques
	for childhood leukemia incidence in Ohio, 1996 - 2003}},
  journal = {International Journal of Health Geographics},
  year = {2007},
  volume = {6},
  pages = {13+},
  number = {1},
  month = {March},
  abstract = {{BACKGROUND:Spatial cluster detection is an important tool in cancer
	surveillance to identify areas of elevated risk and to generate hypotheses
	about cancer etiology. There are many cluster detection methods used
	in spatial epidemiology to investigate suspicious groupings of cancer
	occurrences in regional count data and case-control data, where controls
	are sampled from the at-risk population. Numerous studies in the
	literature have focused on childhood leukemia because of its relatively
	large incidence among children compared with other malignant diseases
	and substantial public concern over elevated leukemia incidence.
	The main focus of this paper is an analysis of the spatial distribution
	of leukemia incidence among children from 0 to 14 years of age in
	Ohio from 1996-2003 using individual case data from the Ohio Cancer
	Incidence Surveillance System (OCISS).Specifically, we explore whether
	there is statistically significant global clustering and if there
	are statistically significant local clusters of individual leukemia
	cases in Ohio using numerous published methods of spatial cluster
	detection, including spatial point process summary methods, a nearest
	neighbor method, and a local rate scanning method. We use the K function,
	Cuzick and Edward's method, and the kernel intensity function to
	test for significant global clustering and the kernel intensity function
	and Kulldorff's spatial scan statistic in SaTScan to test for significant
	local clusters.RESULTS:We found some evidence, although inconclusive,
	of significant local clusters in childhood leukemia in Ohio, but
	no significant overall clustering. The findings from the local cluster
	detection analyses are not consistent for the different cluster detection
	techniques, where the spatial scan method in SaTScan does not find
	statistically significant local clusters, while the kernel intensity
	function method suggests statistically significant clusters in areas
	of central, southern, and eastern Ohio. The findings are consistent
	for the different tests of global clustering, where no significant
	clustering is demonstrated with any of the techniques when all age
	cases are considered together.CONCLUSION:This comparative study for
	childhood leukemia clustering and clusters in Ohio revealed several
	research issues in practical spatial cluster detection. Among them,
	flexibility in cluster shape detection should be an issue for consideration.}},
  citeulike-article-id = {1190374},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-6-13},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/17389045},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=17389045},
  day = {27},
  doi = {10.1186/1476-072X-6-13},
  issn = {1476-072X},
  keywords = {cancer, clustering, disease, epidemiology, spatial, spatial\_analysis},
  posted-at = {2007-03-28 18:37:48},
  priority = {2},
  url = {@article{citeulike:8653754, abstract = {{This paper compares the performances of three exploratory methods for cluster detection in spatial point patterns where the at-risk population is known. After reviewing two existing methods, Openshaw et al. (1987) and Besag and Newell (1991), an alternative method is introduced. These three methods are then compared empirically using two point patterns drawn from a disaggregate housing database consisting of 28,832 observations. Each observation in the data set contains attributes of single-family detached dwellings in the City of Amherst, New York. This paper provides some new insights into the performance of the three methods, as previous applications have used spatially aggregated (and hence rather inaccurate) data. The paper also demonstrates the utility of GIS for this type of spatial analysis.}}, author = {Fotheringham, A. Stewart and Zhan, F. Benjamin}, citeulike-article-id = {8653754}, citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1538-4632.1996.tb00931.x}, doi = {10.1111/j.1538-4632.1996.tb00931.x}, journal = {Geographical Analysis}, keywords = {cluster\_detection, clustering, gam, health}, number = {3}, pages = {200--218}, posted-at = {2011-01-20 00:01:15}, priority = {0}, publisher = {Blackwell Publishing Ltd}, title = {{A Comparison of Three Exploratory Methods for Cluster Detection in Spatial Point Patterns}}, url = {http://dx.doi.org/10.1111/j.1538-4632.1996.tb00931.x}, volume = {28}, year = {1996} }}
}

@ARTICLE{citeulike:3795770,
  author = {Woodring, Jonathan and Shen, Han-Wei},
  title = {{Multiscale Time Activity Data Exploration via Temporal Clustering
	Visualization Spreadsheet}},
  journal = {Visualization and Computer Graphics, IEEE Transactions on},
  year = {2009},
  volume = {15},
  pages = {123--137},
  number = {1},
  abstract = {{Time-varying data is usually explored by animation or arrays of static
	images. Neither is particularly effective for classifying data by
	different temporal activities. Important temporal trends can be missed
	due to the lack of ability to find them with current visualization
	methods. In this paper, we propose a method to explore data at different
	temporal resolutions to discover and highlight data based upon time-varying
	trends. Using the wavelet transform along the time axis, we transform
	data points into multi-scale time series curve sets. The time curves
	are clustered so that data of similar activity are grouped together,
	at different temporal resolutions. The data are displayed to the
	user in a global time view spreadsheet where she is able to select
	temporal clusters of data points, and filter and brush data across
	temporal scales. With our method, a user can interact with data based
	on time activities and create expressive visualizations.}},
  booktitle = {Visualization and Computer Graphics, IEEE Transactions on},
  citeulike-article-id = {3795770},
  citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2008.69},
  citeulike-linkout-1 = {http://dx.doi.org/10.1109/TVCG.2008.69},
  citeulike-linkout-2 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=4515862},
  doi = {10.1109/TVCG.2008.69},
  keywords = {clustering, infovis, temporal, visualization},
  posted-at = {2008-12-16 13:39:27},
  priority = {2},
  url = {http://dx.doi.org/10.1109/TVCG.2008.69}
}

@ARTICLE{citeulike:1392759,
  author = {Yamada, Ikuho and Thill, Jean-Claude},
  title = {{Local Indicators of Network-Constrained Clusters in Spatial Point
	Patterns}},
  journal = {Geographical Analysis},
  year = {2007},
  volume = {39},
  pages = {268--292},
  number = {3},
  month = {July},
  citeulike-article-id = {1392759},
  citeulike-linkout-0 = {http://dx.doi.org/10.1111/j.1538-4632.2007.00704.x},
  citeulike-linkout-1 = {http://www.ingentaconnect.com/content/bsc/gean/2007/00000039/00000003/art00002},
  doi = {10.1111/j.1538-4632.2007.00704.x},
  issn = {0016-7363},
  keywords = {algorithm, analysis, clustering, geospatial, networks},
  posted-at = {2007-06-19 20:39:24},
  priority = {3},
  publisher = {Blackwell Publishing},
  url = {http://dx.doi.org/10.1111/j.1538-4632.2007.00704.x}
}

@ARTICLE{citeulike:1440950,
  author = {Yiannakoulias, Nikolaos and Rosychuk, Rhonda J. and Hodgson, John},
  title = {{Adaptations for finding irregularly shaped disease clusters}},
  journal = {International Journal of Health Geographics},
  year = {2007},
  volume = {6},
  pages = {28+},
  month = {July},
  citeulike-article-id = {1440950},
  citeulike-linkout-0 = {http://dx.doi.org/10.1186/1476-072X-6-28},
  day = {05},
  doi = {10.1186/1476-072X-6-28},
  issn = {1476-072X},
  keywords = {algorithm, clustering, discovery, disease, health},
  posted-at = {2007-07-09 16:17:47},
  priority = {0},
  url = {http://dx.doi.org/10.1186/1476-072X-6-28}
}

@INPROCEEDINGS{citeulike:1987708,
  author = {Zhang, Kuo and Zi, Juan and Wu, Li G.},
  title = {{New event detection based on indexing-tree and named entity}},
  booktitle = {SIGIR '07: Proceedings of the 30th annual international ACM SIGIR
	conference on Research and development in information retrieval},
  year = {2007},
  pages = {215--222},
  address = {New York, NY, USA},
  publisher = {ACM},
  abstract = {{New Event Detection (NED) aims at detecting from one or multiple
	streams of news stories that which one is reported on a new event
	(i.e. not reported previously). With the overwhelming volume of news
	available today, there is an increasing need for a NED system which
	is able to detect new events more efficiently and accurately. In
	this paper we propose a new NED model to speed up the NED task by
	using news indexing-tree dynamically. Moreover, based on the observation
	that terms of different types have different effects for NED task,
	two term reweighting approaches are proposed to improve NED accuracy.
	In the first approach, we propose to adjust term weights dynamically
	based on previous story clusters and in the second approach, we propose
	to employ statistics on training data to learn the named entity reweighting
	model for each class of stories. Experimental results on two Linguistic
	Data Consortium (LDC) datasets TDT2 and TDT3 show that the proposed
	model can improve both efficiency and accuracy of NED task significantly,
	compared to the baseline system and other existing systems.}},
  citeulike-article-id = {1987708},
  citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1277780},
  citeulike-linkout-1 = {http://dx.doi.org/10.1145/1277741.1277780},
  doi = {10.1145/1277741.1277780},
  isbn = {978-1-59593-597-7},
  keywords = {clustering, events, ner, news, nlp, text-mining},
  location = {Amsterdam, The Netherlands},
  posted-at = {2009-09-18 16:48:59},
  priority = {2},
  url = {http://dx.doi.org/10.1145/1277741.1277780}
}

@BOOK{citeulike:2315125,
  title = {{Methods for investigating localized clustering of disease}},
  publisher = {International Agency for Research on Cancer},
  year = {1996},
  editor = {Alexander, F. E. and Boyle, P.},
  address = {World Health Organization, Lyon, France},
  citeulike-article-id = {2315125},
  citeulike-linkout-0 = {http://worldcat.org/wcpa/oclc/37902532},
  keywords = {analysis, cancer, clustering, epidemiology, gam, geographic},
  posted-at = {2008-01-31 14:34:08},
  priority = {0},
  url = {http://worldcat.org/wcpa/oclc/37902532}
}

